Overview

Brought to you by YData

Dataset statistics

Number of variables67
Number of observations4346
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 MiB
Average record size in memory1.5 KiB

Variable types

Text5
Categorical32
Numeric18
DateTime3
Boolean9

Alerts

Type has constant value "0" Constant
Claim_amount is highly overall correlated with Deductibles and 6 other fieldsHigh correlation
Deductibles is highly overall correlated with Claim_amount and 4 other fieldsHigh correlation
Depreciation is highly overall correlated with Insured_amount and 3 other fieldsHigh correlation
Insurance_settlement_amount is highly overall correlated with Claim_amount and 6 other fieldsHigh correlation
Insured_amount is highly overall correlated with Claim_amount and 8 other fieldsHigh correlation
Premium_amount is highly overall correlated with Claim_amount and 6 other fieldsHigh correlation
Repair_Cost is highly overall correlated with Claim_amount and 6 other fieldsHigh correlation
Repair_estimates is highly overall correlated with Claim_amount and 6 other fieldsHigh correlation
Salvage_value is highly overall correlated with Depreciation and 2 other fieldsHigh correlation
Vehicle_value_market_value is highly overall correlated with Claim_amount and 7 other fieldsHigh correlation
Policy_number has unique values Unique
Vehicle_value_market_value has unique values Unique
Repair_estimates has unique values Unique
Repair_Cost has unique values Unique
Depreciation has unique values Unique
Salvage_value has unique values Unique
Driving_License has unique values Unique
RC_Book has unique values Unique
Driver_license has unique values Unique
Previous_claims has 742 (17.1%) zeros Zeros

Reproduction

Analysis started2025-03-22 16:55:23.782804
Analysis finished2025-03-22 16:56:45.344932
Duration1 minute and 21.56 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Policy_number
Text

Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size288.6 KiB
2025-03-22T22:26:45.829552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters47806
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4346 ?
Unique (%)100.0%

Sample

1st rowPOL91142091
2nd rowPOL99025488
3rd rowPOL23635842
4th rowPOL67645878
5th rowPOL63547538
ValueCountFrequency (%)
pol91142091 1
 
< 0.1%
pol59745890 1
 
< 0.1%
pol31065756 1
 
< 0.1%
pol68397232 1
 
< 0.1%
pol23635842 1
 
< 0.1%
pol67645878 1
 
< 0.1%
pol63547538 1
 
< 0.1%
pol63652428 1
 
< 0.1%
pol79963512 1
 
< 0.1%
pol95930571 1
 
< 0.1%
Other values (4336) 4336
99.8%
2025-03-22T22:26:46.541833image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 4346
9.1%
O 4346
9.1%
L 4346
9.1%
6 3629
 
7.6%
3 3567
 
7.5%
4 3542
 
7.4%
0 3481
 
7.3%
9 3479
 
7.3%
2 3450
 
7.2%
8 3437
 
7.2%
Other values (3) 10183
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 4346
9.1%
O 4346
9.1%
L 4346
9.1%
6 3629
 
7.6%
3 3567
 
7.5%
4 3542
 
7.4%
0 3481
 
7.3%
9 3479
 
7.3%
2 3450
 
7.2%
8 3437
 
7.2%
Other values (3) 10183
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 4346
9.1%
O 4346
9.1%
L 4346
9.1%
6 3629
 
7.6%
3 3567
 
7.5%
4 3542
 
7.4%
0 3481
 
7.3%
9 3479
 
7.3%
2 3450
 
7.2%
8 3437
 
7.2%
Other values (3) 10183
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 4346
9.1%
O 4346
9.1%
L 4346
9.1%
6 3629
 
7.6%
3 3567
 
7.5%
4 3542
 
7.4%
0 3481
 
7.3%
9 3479
 
7.3%
2 3450
 
7.2%
8 3437
 
7.2%
Other values (3) 10183
21.3%

Type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
4346 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4346
100.0%

Length

2025-03-22T22:26:46.816547image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:47.026162image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4346
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4346
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4346
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4346
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4346
100.0%

Coverage
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
1486 
2
1439 
0
1421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1486
34.2%
2 1439
33.1%
0 1421
32.7%

Length

2025-03-22T22:26:47.195927image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:47.404030image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1486
34.2%
2 1439
33.1%
0 1421
32.7%

Most occurring characters

ValueCountFrequency (%)
1 1486
34.2%
2 1439
33.1%
0 1421
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1486
34.2%
2 1439
33.1%
0 1421
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1486
34.2%
2 1439
33.1%
0 1421
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1486
34.2%
2 1439
33.1%
0 1421
32.7%

Coverage_Add_Ons
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
904 
1
873 
4
871 
2
850 
3
848 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
0 904
20.8%
1 873
20.1%
4 871
20.0%
2 850
19.6%
3 848
19.5%

Length

2025-03-22T22:26:47.625822image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:47.836051image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 904
20.8%
1 873
20.1%
4 871
20.0%
2 850
19.6%
3 848
19.5%

Most occurring characters

ValueCountFrequency (%)
0 904
20.8%
1 873
20.1%
4 871
20.0%
2 850
19.6%
3 848
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 904
20.8%
1 873
20.1%
4 871
20.0%
2 850
19.6%
3 848
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 904
20.8%
1 873
20.1%
4 871
20.0%
2 850
19.6%
3 848
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 904
20.8%
1 873
20.1%
4 871
20.0%
2 850
19.6%
3 848
19.5%

Policy_Status
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
1497 
2
1444 
0
1405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1497
34.4%
2 1444
33.2%
0 1405
32.3%

Length

2025-03-22T22:26:48.114661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:48.283044image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1497
34.4%
2 1444
33.2%
0 1405
32.3%

Most occurring characters

ValueCountFrequency (%)
1 1497
34.4%
2 1444
33.2%
0 1405
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1497
34.4%
2 1444
33.2%
0 1405
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1497
34.4%
2 1444
33.2%
0 1405
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1497
34.4%
2 1444
33.2%
0 1405
32.3%

Insured_amount
Real number (ℝ)

High correlation 

Distinct4341
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1004247.6
Minimum200108
Maximum1999703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:26:48.563125image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum200108
5-th percentile282840.25
Q1586583.25
median986946.5
Q31393596.8
95-th percentile1825365.8
Maximum1999703
Range1799595
Interquartile range (IQR)807013.5

Descriptive statistics

Standard deviation484971.36
Coefficient of variation (CV)0.4829201
Kurtosis-1.0470319
Mean1004247.6
Median Absolute Deviation (MAD)404298
Skewness0.17989964
Sum4.3644602 × 109
Variance2.3519722 × 1011
MonotonicityNot monotonic
2025-03-22T22:26:48.932747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1489199 2
 
< 0.1%
1553777 2
 
< 0.1%
1475051 2
 
< 0.1%
1027162 2
 
< 0.1%
556229 2
 
< 0.1%
752363 1
 
< 0.1%
269457 1
 
< 0.1%
674856 1
 
< 0.1%
656823 1
 
< 0.1%
318301 1
 
< 0.1%
Other values (4331) 4331
99.7%
ValueCountFrequency (%)
200108 1
< 0.1%
200453 1
< 0.1%
200724 1
< 0.1%
200802 1
< 0.1%
201441 1
< 0.1%
201827 1
< 0.1%
202299 1
< 0.1%
203205 1
< 0.1%
203215 1
< 0.1%
203280 1
< 0.1%
ValueCountFrequency (%)
1999703 1
< 0.1%
1998589 1
< 0.1%
1998462 1
< 0.1%
1998183 1
< 0.1%
1995837 1
< 0.1%
1995534 1
< 0.1%
1993575 1
< 0.1%
1992828 1
< 0.1%
1992787 1
< 0.1%
1990502 1
< 0.1%

Premium_amount
Real number (ℝ)

High correlation 

Distinct4169
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-1.4584839
Maximum3.1764167
Zeros0
Zeros (%)0.0%
Negative2537
Negative (%)58.4%
Memory size67.9 KiB
2025-03-22T22:26:49.244618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4584839
5-th percentile-1.2369283
Q1-0.79817476
median-0.23474288
Q30.63874269
95-th percentile1.973352
Maximum3.1764167
Range4.6349006
Interquartile range (IQR)1.4369175

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)nan
Kurtosis-0.081381812
Mean0
Median Absolute Deviation (MAD)0.66339956
Skewness0.8039111
Sum3.952394 × 10-14
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:26:49.551705image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08601747913 3
 
0.1%
-0.3505766045 3
 
0.1%
-0.7843776445 3
 
0.1%
-0.010394246 3
 
0.1%
-0.841479868 3
 
0.1%
-0.6217604429 3
 
0.1%
0.5511626744 2
 
< 0.1%
-0.5503826636 2
 
< 0.1%
0.1103273025 2
 
< 0.1%
-1.28009115 2
 
< 0.1%
Other values (4159) 4320
99.4%
ValueCountFrequency (%)
-1.458483875 1
< 0.1%
-1.457656307 1
< 0.1%
-1.443484197 1
< 0.1%
-1.442760074 1
< 0.1%
-1.423932892 1
< 0.1%
-1.41808819 1
< 0.1%
-1.415967546 1
< 0.1%
-1.409657336 1
< 0.1%
-1.408674599 1
< 0.1%
-1.402674727 1
< 0.1%
ValueCountFrequency (%)
3.176416709 1
< 0.1%
3.162968722 1
< 0.1%
3.15200344 1
< 0.1%
3.144141539 1
< 0.1%
3.140676096 1
< 0.1%
3.119573101 1
< 0.1%
3.093142633 1
< 0.1%
3.090556482 1
< 0.1%
3.086832424 1
< 0.1%
3.079280862 1
< 0.1%
Distinct366
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2024-03-18 00:00:00
Maximum2025-03-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-22T22:26:49.802059image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:50.131060image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Payment_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
2178 
0
2168 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2178
50.1%
0 2168
49.9%

Length

2025-03-22T22:26:50.374295image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:50.628018image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2178
50.1%
0 2168
49.9%

Most occurring characters

ValueCountFrequency (%)
1 2178
50.1%
0 2168
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2178
50.1%
0 2168
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2178
50.1%
0 2168
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2178
50.1%
0 2168
49.9%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
910 
1
891 
2
858 
4
849 
3
838 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 910
20.9%
1 891
20.5%
2 858
19.7%
4 849
19.5%
3 838
19.3%

Length

2025-03-22T22:26:50.874537image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:51.141492image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 910
20.9%
1 891
20.5%
2 858
19.7%
4 849
19.5%
3 838
19.3%

Most occurring characters

ValueCountFrequency (%)
0 910
20.9%
1 891
20.5%
2 858
19.7%
4 849
19.5%
3 838
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 910
20.9%
1 891
20.5%
2 858
19.7%
4 849
19.5%
3 838
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 910
20.9%
1 891
20.5%
2 858
19.7%
4 849
19.5%
3 838
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 910
20.9%
1 891
20.5%
2 858
19.7%
4 849
19.5%
3 838
19.3%
Distinct726
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2025-03-19 00:00:00
Maximum2027-03-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-22T22:26:51.492887image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:51.792177image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Claimant_age
Real number (ℝ)

Distinct53
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.88081
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:26:52.079951image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median44
Q357
95-th percentile68
Maximum70
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.19109
Coefficient of variation (CV)0.34618983
Kurtosis-1.1844569
Mean43.88081
Median Absolute Deviation (MAD)13
Skewness0.019610813
Sum190706
Variance230.76922
MonotonicityNot monotonic
2025-03-22T22:26:52.417345image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 110
 
2.5%
47 101
 
2.3%
32 99
 
2.3%
34 98
 
2.3%
19 96
 
2.2%
69 95
 
2.2%
49 94
 
2.2%
66 94
 
2.2%
54 94
 
2.2%
53 93
 
2.1%
Other values (43) 3372
77.6%
ValueCountFrequency (%)
18 77
1.8%
19 96
2.2%
20 69
1.6%
21 76
1.7%
22 76
1.7%
23 79
1.8%
24 90
2.1%
25 81
1.9%
26 90
2.1%
27 59
1.4%
ValueCountFrequency (%)
70 75
1.7%
69 95
2.2%
68 72
1.7%
67 69
1.6%
66 94
2.2%
65 79
1.8%
64 78
1.8%
63 82
1.9%
62 75
1.7%
61 83
1.9%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size295.7 KiB
1 accident
1104 
Clean
1100 
2 minor accidents
1075 
One speeding ticket
1067 

Length

Max length19
Median length17
Mean length12.675564
Min length5

Characters and Unicode

Total characters55088
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 accident
2nd row2 minor accidents
3rd rowClean
4th rowOne speeding ticket
5th row2 minor accidents

Common Values

ValueCountFrequency (%)
1 accident 1104
25.4%
Clean 1100
25.3%
2 minor accidents 1075
24.7%
One speeding ticket 1067
24.6%

Length

2025-03-22T22:26:52.817490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:53.025195image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1104
11.3%
accident 1104
11.3%
clean 1100
11.3%
2 1075
11.0%
minor 1075
11.0%
accidents 1075
11.0%
one 1067
11.0%
speeding 1067
11.0%
ticket 1067
11.0%

Most occurring characters

ValueCountFrequency (%)
e 7547
13.7%
n 6488
11.8%
c 5425
9.8%
5388
9.8%
i 5388
9.8%
t 4313
7.8%
a 3279
 
6.0%
d 3246
 
5.9%
s 2142
 
3.9%
1 1104
 
2.0%
Other values (10) 10768
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7547
13.7%
n 6488
11.8%
c 5425
9.8%
5388
9.8%
i 5388
9.8%
t 4313
7.8%
a 3279
 
6.0%
d 3246
 
5.9%
s 2142
 
3.9%
1 1104
 
2.0%
Other values (10) 10768
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7547
13.7%
n 6488
11.8%
c 5425
9.8%
5388
9.8%
i 5388
9.8%
t 4313
7.8%
a 3279
 
6.0%
d 3246
 
5.9%
s 2142
 
3.9%
1 1104
 
2.0%
Other values (10) 10768
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7547
13.7%
n 6488
11.8%
c 5425
9.8%
5388
9.8%
i 5388
9.8%
t 4313
7.8%
a 3279
 
6.0%
d 3246
 
5.9%
s 2142
 
3.9%
1 1104
 
2.0%
Other values (10) 10768
19.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
2229 
0
2117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2229
51.3%
0 2117
48.7%

Length

2025-03-22T22:26:53.223931image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:53.397053image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2229
51.3%
0 2117
48.7%

Most occurring characters

ValueCountFrequency (%)
1 2229
51.3%
0 2117
48.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2229
51.3%
0 2117
48.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2229
51.3%
0 2117
48.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2229
51.3%
0 2117
48.7%

Vehicle_value_market_value
Real number (ℝ)

High correlation  Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8286488 × 10-17
Minimum-1.5985397
Maximum3.39112
Zeros0
Zeros (%)0.0%
Negative2406
Negative (%)55.4%
Memory size67.9 KiB
2025-03-22T22:26:53.635076image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5985397
5-th percentile-1.3447857
Q1-0.7970125
median-0.15084729
Q30.67914552
95-th percentile1.863378
Maximum3.39112
Range4.9896597
Interquartile range (IQR)1.476158

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)1.1328065 × 1016
Kurtosis-0.14088383
Mean8.8286488 × 10-17
Median Absolute Deviation (MAD)0.71597388
Skewness0.64892893
Sum3.539391 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:26:53.906667image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.136914631 1
 
< 0.1%
-0.4915389816 1
 
< 0.1%
-1.177561869 1
 
< 0.1%
0.7180126071 1
 
< 0.1%
0.8884618417 1
 
< 0.1%
-0.106552078 1
 
< 0.1%
0.3767306061 1
 
< 0.1%
-0.4483235273 1
 
< 0.1%
-1.061619095 1
 
< 0.1%
-1.204931932 1
 
< 0.1%
Other values (4336) 4336
99.8%
ValueCountFrequency (%)
-1.598539653 1
< 0.1%
-1.591589039 1
< 0.1%
-1.591487472 1
< 0.1%
-1.580184523 1
< 0.1%
-1.575394533 1
< 0.1%
-1.572761281 1
< 0.1%
-1.571551569 1
< 0.1%
-1.564421513 1
< 0.1%
-1.561832832 1
< 0.1%
-1.561571016 1
< 0.1%
ValueCountFrequency (%)
3.391120036 1
< 0.1%
3.345056984 1
< 0.1%
3.344919082 1
< 0.1%
3.302176458 1
< 0.1%
3.297607849 1
< 0.1%
3.261097341 1
< 0.1%
3.169311903 1
< 0.1%
3.159476679 1
< 0.1%
3.136914631 1
< 0.1%
3.127073204 1
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
2
1473 
0
1438 
1
1435 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 1473
33.9%
0 1438
33.1%
1 1435
33.0%

Length

2025-03-22T22:26:54.118563image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:54.339242image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1473
33.9%
0 1438
33.1%
1 1435
33.0%

Most occurring characters

ValueCountFrequency (%)
2 1473
33.9%
0 1438
33.1%
1 1435
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1473
33.9%
0 1438
33.1%
1 1435
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1473
33.9%
0 1438
33.1%
1 1435
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1473
33.9%
0 1438
33.1%
1 1435
33.0%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size284.4 KiB
No accidents
1452 
1 accident
1449 
1 repair
1445 

Length

Max length12
Median length10
Mean length10.003221
Min length8

Characters and Unicode

Total characters43474
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 accident
2nd rowNo accidents
3rd row1 repair
4th row1 accident
5th row1 repair

Common Values

ValueCountFrequency (%)
No accidents 1452
33.4%
1 accident 1449
33.3%
1 repair 1445
33.2%

Length

2025-03-22T22:26:54.579673image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:54.824353image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2894
33.3%
no 1452
16.7%
accidents 1452
16.7%
accident 1449
16.7%
repair 1445
16.6%

Most occurring characters

ValueCountFrequency (%)
c 5802
13.3%
4346
10.0%
a 4346
10.0%
i 4346
10.0%
e 4346
10.0%
d 2901
6.7%
n 2901
6.7%
t 2901
6.7%
1 2894
6.7%
r 2890
6.6%
Other values (4) 5801
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 5802
13.3%
4346
10.0%
a 4346
10.0%
i 4346
10.0%
e 4346
10.0%
d 2901
6.7%
n 2901
6.7%
t 2901
6.7%
1 2894
6.7%
r 2890
6.6%
Other values (4) 5801
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 5802
13.3%
4346
10.0%
a 4346
10.0%
i 4346
10.0%
e 4346
10.0%
d 2901
6.7%
n 2901
6.7%
t 2901
6.7%
1 2894
6.7%
r 2890
6.6%
Other values (4) 5801
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 5802
13.3%
4346
10.0%
a 4346
10.0%
i 4346
10.0%
e 4346
10.0%
d 2901
6.7%
n 2901
6.7%
t 2901
6.7%
1 2894
6.7%
r 2890
6.6%
Other values (4) 5801
13.3%

Fuel_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
2252 
0
2094 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2252
51.8%
0 2094
48.2%

Length

2025-03-22T22:26:55.034657image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:55.179591image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2252
51.8%
0 2094
48.2%

Most occurring characters

ValueCountFrequency (%)
1 2252
51.8%
0 2094
48.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2252
51.8%
0 2094
48.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2252
51.8%
0 2094
48.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2252
51.8%
0 2094
48.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
2180 
1
2166 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2180
50.2%
1 2166
49.8%

Length

2025-03-22T22:26:55.352944image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:55.542107image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2180
50.2%
1 2166
49.8%

Most occurring characters

ValueCountFrequency (%)
0 2180
50.2%
1 2166
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2180
50.2%
1 2166
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2180
50.2%
1 2166
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2180
50.2%
1 2166
49.8%

Odometer_reading
Real number (ℝ)

Distinct4228
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54718.543
Minimum10025
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:26:55.730628image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum10025
5-th percentile14384.5
Q131711.75
median54421.5
Q377507.75
95-th percentile95513
Maximum99990
Range89965
Interquartile range (IQR)45796

Descriptive statistics

Standard deviation26047.14
Coefficient of variation (CV)0.47602034
Kurtosis-1.2067349
Mean54718.543
Median Absolute Deviation (MAD)22904.5
Skewness0.023154418
Sum2.3780679 × 108
Variance6.7845349 × 108
MonotonicityNot monotonic
2025-03-22T22:26:56.041092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14029 3
 
0.1%
80438 3
 
0.1%
91186 3
 
0.1%
56122 2
 
< 0.1%
72620 2
 
< 0.1%
99571 2
 
< 0.1%
29022 2
 
< 0.1%
42593 2
 
< 0.1%
92328 2
 
< 0.1%
83570 2
 
< 0.1%
Other values (4218) 4323
99.5%
ValueCountFrequency (%)
10025 1
< 0.1%
10032 1
< 0.1%
10034 1
< 0.1%
10067 1
< 0.1%
10077 1
< 0.1%
10096 2
< 0.1%
10135 1
< 0.1%
10156 1
< 0.1%
10169 1
< 0.1%
10181 1
< 0.1%
ValueCountFrequency (%)
99990 1
< 0.1%
99961 1
< 0.1%
99932 1
< 0.1%
99908 1
< 0.1%
99896 1
< 0.1%
99881 1
< 0.1%
99871 1
< 0.1%
99824 2
< 0.1%
99807 1
< 0.1%
99797 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size334.1 KiB
Airbags, ABS, EBD
1121 
Airbags, ABS, Reverse Sensors
1098 
Airbags, ABS
1067 
Airbags, ABS, Parking Sensors
1060 

Length

Max length29
Median length17
Mean length21.731017
Min length12

Characters and Unicode

Total characters94443
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAirbags, ABS
2nd rowAirbags, ABS
3rd rowAirbags, ABS
4th rowAirbags, ABS, Parking Sensors
5th rowAirbags, ABS

Common Values

ValueCountFrequency (%)
Airbags, ABS, EBD 1121
25.8%
Airbags, ABS, Reverse Sensors 1098
25.3%
Airbags, ABS 1067
24.6%
Airbags, ABS, Parking Sensors 1060
24.4%

Length

2025-03-22T22:26:56.343857image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:56.618007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
airbags 4346
30.8%
abs 4346
30.8%
sensors 2158
15.3%
ebd 1121
 
7.9%
reverse 1098
 
7.8%
parking 1060
 
7.5%

Most occurring characters

ValueCountFrequency (%)
9783
10.4%
s 9760
10.3%
A 8692
9.2%
r 8662
 
9.2%
, 7625
 
8.1%
S 6504
 
6.9%
B 5467
 
5.8%
e 5452
 
5.8%
g 5406
 
5.7%
a 5406
 
5.7%
Other values (10) 21686
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9783
10.4%
s 9760
10.3%
A 8692
9.2%
r 8662
 
9.2%
, 7625
 
8.1%
S 6504
 
6.9%
B 5467
 
5.8%
e 5452
 
5.8%
g 5406
 
5.7%
a 5406
 
5.7%
Other values (10) 21686
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9783
10.4%
s 9760
10.3%
A 8692
9.2%
r 8662
 
9.2%
, 7625
 
8.1%
S 6504
 
6.9%
B 5467
 
5.8%
e 5452
 
5.8%
g 5406
 
5.7%
a 5406
 
5.7%
Other values (10) 21686
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9783
10.4%
s 9760
10.3%
A 8692
9.2%
r 8662
 
9.2%
, 7625
 
8.1%
S 6504
 
6.9%
B 5467
 
5.8%
e 5452
 
5.8%
g 5406
 
5.7%
a 5406
 
5.7%
Other values (10) 21686
23.0%
Distinct3784
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size293.2 KiB
2025-03-22T22:26:57.186899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length12.079613
Min length6

Characters and Unicode

Total characters52498
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3353 ?
Unique (%)77.2%

Sample

1st rowLake Michael
2nd rowAtkinsontown
3rd rowPort Vanessa
4th rowLake Stephanieville
5th rowNew James
ValueCountFrequency (%)
new 333
 
5.1%
east 331
 
5.1%
west 327
 
5.0%
lake 319
 
4.9%
north 301
 
4.6%
port 299
 
4.6%
south 293
 
4.5%
michael 28
 
0.4%
christopher 27
 
0.4%
jennifer 18
 
0.3%
Other values (3010) 4273
65.2%
2025-03-22T22:26:58.125568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5120
 
9.8%
r 4109
 
7.8%
t 4075
 
7.8%
a 4070
 
7.8%
o 3582
 
6.8%
h 2969
 
5.7%
n 2747
 
5.2%
i 2584
 
4.9%
s 2528
 
4.8%
2203
 
4.2%
Other values (42) 18511
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5120
 
9.8%
r 4109
 
7.8%
t 4075
 
7.8%
a 4070
 
7.8%
o 3582
 
6.8%
h 2969
 
5.7%
n 2747
 
5.2%
i 2584
 
4.9%
s 2528
 
4.8%
2203
 
4.2%
Other values (42) 18511
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5120
 
9.8%
r 4109
 
7.8%
t 4075
 
7.8%
a 4070
 
7.8%
o 3582
 
6.8%
h 2969
 
5.7%
n 2747
 
5.2%
i 2584
 
4.9%
s 2528
 
4.8%
2203
 
4.2%
Other values (42) 18511
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5120
 
9.8%
r 4109
 
7.8%
t 4075
 
7.8%
a 4070
 
7.8%
o 3582
 
6.8%
h 2969
 
5.7%
n 2747
 
5.2%
i 2584
 
4.9%
s 2528
 
4.8%
2203
 
4.2%
Other values (42) 18511
35.3%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
925 
4
895 
0
850 
3
841 
2
835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row4
5th row2

Common Values

ValueCountFrequency (%)
1 925
21.3%
4 895
20.6%
0 850
19.6%
3 841
19.4%
2 835
19.2%

Length

2025-03-22T22:26:58.416721image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:58.644849image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 925
21.3%
4 895
20.6%
0 850
19.6%
3 841
19.4%
2 835
19.2%

Most occurring characters

ValueCountFrequency (%)
1 925
21.3%
4 895
20.6%
0 850
19.6%
3 841
19.4%
2 835
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 925
21.3%
4 895
20.6%
0 850
19.6%
3 841
19.4%
2 835
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 925
21.3%
4 895
20.6%
0 850
19.6%
3 841
19.4%
2 835
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 925
21.3%
4 895
20.6%
0 850
19.6%
3 841
19.4%
2 835
19.2%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
1476 
2
1442 
0
1428 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 1476
34.0%
2 1442
33.2%
0 1428
32.9%

Length

2025-03-22T22:26:58.963125image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:59.212052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1476
34.0%
2 1442
33.2%
0 1428
32.9%

Most occurring characters

ValueCountFrequency (%)
1 1476
34.0%
2 1442
33.2%
0 1428
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1476
34.0%
2 1442
33.2%
0 1428
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1476
34.0%
2 1442
33.2%
0 1428
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1476
34.0%
2 1442
33.2%
0 1428
32.9%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
3
882 
1
876 
0
876 
2
860 
4
852 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row4
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 882
20.3%
1 876
20.2%
0 876
20.2%
2 860
19.8%
4 852
19.6%

Length

2025-03-22T22:26:59.488050image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:26:59.702263image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
3 882
20.3%
1 876
20.2%
0 876
20.2%
2 860
19.8%
4 852
19.6%

Most occurring characters

ValueCountFrequency (%)
3 882
20.3%
1 876
20.2%
0 876
20.2%
2 860
19.8%
4 852
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 882
20.3%
1 876
20.2%
0 876
20.2%
2 860
19.8%
4 852
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 882
20.3%
1 876
20.2%
0 876
20.2%
2 860
19.8%
4 852
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 882
20.3%
1 876
20.2%
0 876
20.2%
2 860
19.8%
4 852
19.6%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size267.3 KiB
Light
1466 
Heavy
1443 
Moderate
1437 

Length

Max length8
Median length5
Mean length5.9919466
Min length5

Characters and Unicode

Total characters26041
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowLight
3rd rowLight
4th rowModerate
5th rowHeavy

Common Values

ValueCountFrequency (%)
Light 1466
33.7%
Heavy 1443
33.2%
Moderate 1437
33.1%

Length

2025-03-22T22:26:59.984111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:00.186062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
light 1466
33.7%
heavy 1443
33.2%
moderate 1437
33.1%

Most occurring characters

ValueCountFrequency (%)
e 4317
16.6%
t 2903
11.1%
a 2880
11.1%
L 1466
 
5.6%
i 1466
 
5.6%
g 1466
 
5.6%
h 1466
 
5.6%
H 1443
 
5.5%
v 1443
 
5.5%
y 1443
 
5.5%
Other values (4) 5748
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4317
16.6%
t 2903
11.1%
a 2880
11.1%
L 1466
 
5.6%
i 1466
 
5.6%
g 1466
 
5.6%
h 1466
 
5.6%
H 1443
 
5.5%
v 1443
 
5.5%
y 1443
 
5.5%
Other values (4) 5748
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4317
16.6%
t 2903
11.1%
a 2880
11.1%
L 1466
 
5.6%
i 1466
 
5.6%
g 1466
 
5.6%
h 1466
 
5.6%
H 1443
 
5.5%
v 1443
 
5.5%
y 1443
 
5.5%
Other values (4) 5748
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4317
16.6%
t 2903
11.1%
a 2880
11.1%
L 1466
 
5.6%
i 1466
 
5.6%
g 1466
 
5.6%
h 1466
 
5.6%
H 1443
 
5.5%
v 1443
 
5.5%
y 1443
 
5.5%
Other values (4) 5748
22.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size300.5 KiB
Rear-ended car
895 
Pedestrian hit
889 
Scratched car
861 
Hit by a truck
855 
Stolen vehicle
846 

Length

Max length14
Median length14
Mean length13.801887
Min length13

Characters and Unicode

Total characters59983
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScratched car
2nd rowHit by a truck
3rd rowRear-ended car
4th rowPedestrian hit
5th rowPedestrian hit

Common Values

ValueCountFrequency (%)
Rear-ended car 895
20.6%
Pedestrian hit 889
20.5%
Scratched car 861
19.8%
Hit by a truck 855
19.7%
Stolen vehicle 846
19.5%

Length

2025-03-22T22:27:00.444608image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:00.697979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
car 1756
16.9%
hit 1744
16.8%
rear-ended 895
8.6%
pedestrian 889
8.5%
scratched 861
8.3%
by 855
8.2%
a 855
8.2%
truck 855
8.2%
stolen 846
8.1%
vehicle 846
8.1%

Most occurring characters

ValueCountFrequency (%)
e 7862
13.1%
6056
10.1%
a 5256
8.8%
r 5256
8.8%
t 5195
8.7%
c 5179
8.6%
d 3540
 
5.9%
i 3479
 
5.8%
n 2630
 
4.4%
h 2596
 
4.3%
Other values (13) 12934
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7862
13.1%
6056
10.1%
a 5256
8.8%
r 5256
8.8%
t 5195
8.7%
c 5179
8.6%
d 3540
 
5.9%
i 3479
 
5.8%
n 2630
 
4.4%
h 2596
 
4.3%
Other values (13) 12934
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7862
13.1%
6056
10.1%
a 5256
8.8%
r 5256
8.8%
t 5195
8.7%
c 5179
8.6%
d 3540
 
5.9%
i 3479
 
5.8%
n 2630
 
4.4%
h 2596
 
4.3%
Other values (13) 12934
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7862
13.1%
6056
10.1%
a 5256
8.8%
r 5256
8.8%
t 5195
8.7%
c 5179
8.6%
d 3540
 
5.9%
i 3479
 
5.8%
n 2630
 
4.4%
h 2596
 
4.3%
Other values (13) 12934
21.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
True
2176 
False
2170 
ValueCountFrequency (%)
True 2176
50.1%
False 2170
49.9%
2025-03-22T22:27:00.920192image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
1
1180 
3
1093 
2
1055 
0
1018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row0
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 1180
27.2%
3 1093
25.1%
2 1055
24.3%
0 1018
23.4%

Length

2025-03-22T22:27:01.139232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:01.443623image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1180
27.2%
3 1093
25.1%
2 1055
24.3%
0 1018
23.4%

Most occurring characters

ValueCountFrequency (%)
1 1180
27.2%
3 1093
25.1%
2 1055
24.3%
0 1018
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1180
27.2%
3 1093
25.1%
2 1055
24.3%
0 1018
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1180
27.2%
3 1093
25.1%
2 1055
24.3%
0 1018
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1180
27.2%
3 1093
25.1%
2 1055
24.3%
0 1018
23.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
True
2220 
False
2126 
ValueCountFrequency (%)
True 2220
51.1%
False 2126
48.9%
2025-03-22T22:27:01.702111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Own_Damage
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
False
2254 
True
2092 
ValueCountFrequency (%)
False 2254
51.9%
True 2092
48.1%
2025-03-22T22:27:01.942446image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
True
2183 
False
2163 
ValueCountFrequency (%)
True 2183
50.2%
False 2163
49.8%
2025-03-22T22:27:02.207428image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
False
2217 
True
2129 
ValueCountFrequency (%)
False 2217
51.0%
True 2129
49.0%
2025-03-22T22:27:02.480903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Extent_of_damage
Real number (ℝ)

Distinct4108
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29746.894
Minimum10006
Maximum49998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:27:02.797890image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum10006
5-th percentile11963.5
Q119692.25
median29684.5
Q339438.5
95-th percentile48024.75
Maximum49998
Range39992
Interquartile range (IQR)19746.25

Descriptive statistics

Standard deviation11509.284
Coefficient of variation (CV)0.38690707
Kurtosis-1.176183
Mean29746.894
Median Absolute Deviation (MAD)9908.5
Skewness0.020182686
Sum1.2928 × 108
Variance1.3246361 × 108
MonotonicityNot monotonic
2025-03-22T22:27:03.177841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22999 3
 
0.1%
30215 3
 
0.1%
16732 3
 
0.1%
14357 3
 
0.1%
26829 3
 
0.1%
42007 3
 
0.1%
10934 3
 
0.1%
23596 3
 
0.1%
12384 3
 
0.1%
17498 2
 
< 0.1%
Other values (4098) 4317
99.3%
ValueCountFrequency (%)
10006 1
< 0.1%
10014 1
< 0.1%
10036 2
< 0.1%
10040 1
< 0.1%
10044 1
< 0.1%
10049 1
< 0.1%
10051 1
< 0.1%
10064 1
< 0.1%
10067 1
< 0.1%
10070 1
< 0.1%
ValueCountFrequency (%)
49998 1
< 0.1%
49994 1
< 0.1%
49965 1
< 0.1%
49925 1
< 0.1%
49918 1
< 0.1%
49908 1
< 0.1%
49905 1
< 0.1%
49903 1
< 0.1%
49896 1
< 0.1%
49894 1
< 0.1%

Repair_estimates
Real number (ℝ)

High correlation  Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.356996 × 10-16
Minimum-1.4173501
Maximum3.3166835
Zeros0
Zeros (%)0.0%
Negative2515
Negative (%)57.9%
Memory size67.9 KiB
2025-03-22T22:27:03.534136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4173501
5-th percentile-1.2024627
Q1-0.80401444
median-0.24606611
Q30.63968069
95-th percentile1.9499049
Maximum3.3166835
Range4.7340335
Interquartile range (IQR)1.4436951

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)7.3700663 × 1015
Kurtosis0.15629946
Mean1.356996 × 10-16
Median Absolute Deviation (MAD)0.65958023
Skewness0.87437456
Sum5.3934635 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:03.874433image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.311190672 1
 
< 0.1%
2.946374617 1
 
< 0.1%
-0.7891739754 1
 
< 0.1%
1.319925484 1
 
< 0.1%
-0.3115124506 1
 
< 0.1%
-0.6751129855 1
 
< 0.1%
-0.6397320629 1
 
< 0.1%
-0.9938247734 1
 
< 0.1%
-0.4333457237 1
 
< 0.1%
-0.9757156452 1
 
< 0.1%
Other values (4336) 4336
99.8%
ValueCountFrequency (%)
-1.417350053 1
< 0.1%
-1.415172206 1
< 0.1%
-1.41140797 1
< 0.1%
-1.41076481 1
< 0.1%
-1.400360384 1
< 0.1%
-1.391853553 1
< 0.1%
-1.388896471 1
< 0.1%
-1.388321793 1
< 0.1%
-1.387394767 1
< 0.1%
-1.38334472 1
< 0.1%
ValueCountFrequency (%)
3.316683468 1
< 0.1%
3.310155184 1
< 0.1%
3.289462122 1
< 0.1%
3.283590516 1
< 0.1%
3.266312634 1
< 0.1%
3.263470339 1
< 0.1%
3.26299665 1
< 0.1%
3.257333332 1
< 0.1%
3.255275004 1
< 0.1%
3.233958874 1
< 0.1%

Repair_Cost
Real number (ℝ)

High correlation  Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.356996 × 10-16
Minimum-1.4173501
Maximum3.3166835
Zeros0
Zeros (%)0.0%
Negative2515
Negative (%)57.9%
Memory size67.9 KiB
2025-03-22T22:27:05.048320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4173501
5-th percentile-1.2024627
Q1-0.80401444
median-0.24606611
Q30.63968069
95-th percentile1.9499049
Maximum3.3166835
Range4.7340335
Interquartile range (IQR)1.4436951

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)7.3700663 × 1015
Kurtosis0.15629946
Mean1.356996 × 10-16
Median Absolute Deviation (MAD)0.65958023
Skewness0.87437456
Sum5.3934635 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:05.374364image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.311190672 1
 
< 0.1%
2.946374617 1
 
< 0.1%
-0.7891739754 1
 
< 0.1%
1.319925484 1
 
< 0.1%
-0.3115124506 1
 
< 0.1%
-0.6751129855 1
 
< 0.1%
-0.6397320629 1
 
< 0.1%
-0.9938247734 1
 
< 0.1%
-0.4333457237 1
 
< 0.1%
-0.9757156452 1
 
< 0.1%
Other values (4336) 4336
99.8%
ValueCountFrequency (%)
-1.417350053 1
< 0.1%
-1.415172206 1
< 0.1%
-1.41140797 1
< 0.1%
-1.41076481 1
< 0.1%
-1.400360384 1
< 0.1%
-1.391853553 1
< 0.1%
-1.388896471 1
< 0.1%
-1.388321793 1
< 0.1%
-1.387394767 1
< 0.1%
-1.38334472 1
< 0.1%
ValueCountFrequency (%)
3.316683468 1
< 0.1%
3.310155184 1
< 0.1%
3.289462122 1
< 0.1%
3.283590516 1
< 0.1%
3.266312634 1
< 0.1%
3.263470339 1
< 0.1%
3.26299665 1
< 0.1%
3.257333332 1
< 0.1%
3.255275004 1
< 0.1%
3.233958874 1
< 0.1%

Depreciation
Real number (ℝ)

High correlation  Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.8096098 × 10-18
Minimum-1.4696983
Maximum3.1168323
Zeros0
Zeros (%)0.0%
Negative2517
Negative (%)57.9%
Memory size67.9 KiB
2025-03-22T22:27:05.699754image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4696983
5-th percentile-1.2429688
Q1-0.79474703
median-0.20991552
Q30.58729252
95-th percentile2.0353012
Maximum3.1168323
Range4.5865306
Interquartile range (IQR)1.3820396

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)-1.0195258 × 1017
Kurtosis0.11339274
Mean-9.8096098 × 10-18
Median Absolute Deviation (MAD)0.6573311
Skewness0.85782201
Sum-5.9063865 × 10-14
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:06.016480image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.456685678 1
 
< 0.1%
0.163153053 1
 
< 0.1%
-0.8177138377 1
 
< 0.1%
-0.6365486896 1
 
< 0.1%
0.3758640095 1
 
< 0.1%
-0.9016270958 1
 
< 0.1%
1.238819492 1
 
< 0.1%
0.2811762854 1
 
< 0.1%
-1.170993857 1
 
< 0.1%
-1.130836407 1
 
< 0.1%
Other values (4336) 4336
99.8%
ValueCountFrequency (%)
-1.469698286 1
< 0.1%
-1.444256028 1
< 0.1%
-1.4434137 1
< 0.1%
-1.442747771 1
< 0.1%
-1.441386162 1
< 0.1%
-1.441040445 1
< 0.1%
-1.440579207 1
< 0.1%
-1.439913189 1
< 0.1%
-1.435487351 1
< 0.1%
-1.425647871 1
< 0.1%
ValueCountFrequency (%)
3.116832318 1
< 0.1%
3.108664054 1
< 0.1%
3.089246975 1
< 0.1%
3.08812949 1
< 0.1%
3.077162835 1
< 0.1%
3.069611382 1
< 0.1%
3.064844316 1
< 0.1%
3.064269196 1
< 0.1%
3.06244516 1
< 0.1%
3.061466056 1
< 0.1%

Salvage_value
Real number (ℝ)

High correlation  Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.8857659 × 10-17
Minimum-1.348664
Maximum3.005442
Zeros0
Zeros (%)0.0%
Negative2578
Negative (%)59.3%
Memory size67.9 KiB
2025-03-22T22:27:06.260836image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.348664
5-th percentile-1.1696082
Q1-0.80478138
median-0.24865751
Q30.61514265
95-th percentile2.0740405
Maximum3.005442
Range4.3541059
Interquartile range (IQR)1.419924

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)-1.6992097 × 1016
Kurtosis0.058620549
Mean-5.8857659 × 10-17
Median Absolute Deviation (MAD)0.63871273
Skewness0.90855296
Sum-2.5712765 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:06.583307image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.076175339 1
 
< 0.1%
0.312295237 1
 
< 0.1%
-1.069168633 1
 
< 0.1%
2.265846935 1
 
< 0.1%
1.487336435 1
 
< 0.1%
0.31816587 1
 
< 0.1%
-0.8620166998 1
 
< 0.1%
0.1585132603 1
 
< 0.1%
-1.15290792 1
 
< 0.1%
-0.5948913368 1
 
< 0.1%
Other values (4336) 4336
99.8%
ValueCountFrequency (%)
-1.348663974 1
< 0.1%
-1.344769729 1
< 0.1%
-1.337406488 1
< 0.1%
-1.333617285 1
< 0.1%
-1.325910843 1
< 0.1%
-1.325227122 1
< 0.1%
-1.324823826 1
< 0.1%
-1.322249499 1
< 0.1%
-1.322131786 1
< 0.1%
-1.321395325 1
< 0.1%
ValueCountFrequency (%)
3.005441973 1
< 0.1%
2.984112946 1
< 0.1%
2.973000547 1
< 0.1%
2.97263624 1
< 0.1%
2.970303902 1
< 0.1%
2.933802683 1
< 0.1%
2.93315884 1
< 0.1%
2.923241939 1
< 0.1%
2.920014895 1
< 0.1%
2.910705291 1
< 0.1%

Parts_replaced
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size293.4 KiB
Left fender
754 
Roof panel
745 
Rear bumper
718 
Front left door
715 
Left side mirror
708 

Length

Max length16
Median length15
Mean length12.138748
Min length10

Characters and Unicode

Total characters52755
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoof panel
2nd rowLeft side mirror
3rd rowRear bumper
4th rowLeft fender
5th rowLeft fender

Common Values

ValueCountFrequency (%)
Left fender 754
17.3%
Roof panel 745
17.1%
Rear bumper 718
16.5%
Front left door 715
16.5%
Left side mirror 708
16.3%
Windshield 706
16.2%

Length

2025-03-22T22:27:06.848917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:07.138942image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
left 2177
23.1%
fender 754
 
8.0%
roof 745
 
7.9%
panel 745
 
7.9%
rear 718
 
7.6%
bumper 718
 
7.6%
front 715
 
7.6%
door 715
 
7.6%
side 708
 
7.5%
mirror 708
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e 7280
13.8%
r 5744
10.9%
5063
9.6%
o 4343
 
8.2%
f 3676
 
7.0%
d 3589
 
6.8%
n 2920
 
5.5%
t 2892
 
5.5%
i 2828
 
5.4%
l 2166
 
4.1%
Other values (11) 12254
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7280
13.8%
r 5744
10.9%
5063
9.6%
o 4343
 
8.2%
f 3676
 
7.0%
d 3589
 
6.8%
n 2920
 
5.5%
t 2892
 
5.5%
i 2828
 
5.4%
l 2166
 
4.1%
Other values (11) 12254
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7280
13.8%
r 5744
10.9%
5063
9.6%
o 4343
 
8.2%
f 3676
 
7.0%
d 3589
 
6.8%
n 2920
 
5.5%
t 2892
 
5.5%
i 2828
 
5.4%
l 2166
 
4.1%
Other values (11) 12254
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7280
13.8%
r 5744
10.9%
5063
9.6%
o 4343
 
8.2%
f 3676
 
7.0%
d 3589
 
6.8%
n 2920
 
5.5%
t 2892
 
5.5%
i 2828
 
5.4%
l 2166
 
4.1%
Other values (11) 12254
23.2%

Insurance_settlement_amount
Real number (ℝ)

High correlation 

Distinct4339
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.8857659 × 10-17
Minimum-1.4480838
Maximum3.2084247
Zeros0
Zeros (%)0.0%
Negative2506
Negative (%)57.7%
Memory size67.9 KiB
2025-03-22T22:27:07.535072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4480838
5-th percentile-1.225628
Q1-0.80145226
median-0.21944186
Q30.64641988
95-th percentile1.9818354
Maximum3.2084247
Range4.6565085
Interquartile range (IQR)1.4478721

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)-1.6992097 × 1016
Kurtosis-0.11184735
Mean-5.8857659 × 10-17
Median Absolute Deviation (MAD)0.68661483
Skewness0.78877536
Sum-2.1138646 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:07.854637image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7543460536 2
 
< 0.1%
-0.6520312062 2
 
< 0.1%
-0.7908244126 2
 
< 0.1%
0.7015011297 2
 
< 0.1%
-0.9866646858 2
 
< 0.1%
0.5666276549 2
 
< 0.1%
-0.7318242852 2
 
< 0.1%
-1.325428855 1
 
< 0.1%
-0.1898227105 1
 
< 0.1%
-0.8563199982 1
 
< 0.1%
Other values (4329) 4329
99.6%
ValueCountFrequency (%)
-1.448083791 1
< 0.1%
-1.44764146 1
< 0.1%
-1.446637709 1
< 0.1%
-1.438202801 1
< 0.1%
-1.436981287 1
< 0.1%
-1.436185092 1
< 0.1%
-1.435838032 1
< 0.1%
-1.428008776 1
< 0.1%
-1.427842052 1
< 0.1%
-1.427011831 1
< 0.1%
ValueCountFrequency (%)
3.208424712 1
< 0.1%
3.187294908 1
< 0.1%
3.162194335 1
< 0.1%
3.156365776 1
< 0.1%
3.125831338 1
< 0.1%
3.120502953 1
< 0.1%
3.094800129 1
< 0.1%
3.050713356 1
< 0.1%
3.02556855 1
< 0.1%
2.981631488 1
< 0.1%

Repair_duration
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5687989
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:27:08.055863image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8785117
Coefficient of variation (CV)0.51689992
Kurtosis-1.2222073
Mean5.5687989
Median Absolute Deviation (MAD)3
Skewness-0.032218634
Sum24202
Variance8.2858295
MonotonicityNot monotonic
2025-03-22T22:27:08.292858image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 459
10.6%
9 457
10.5%
10 453
10.4%
7 433
10.0%
1 430
9.9%
8 429
9.9%
3 427
9.8%
5 423
9.7%
4 423
9.7%
2 412
9.5%
ValueCountFrequency (%)
1 430
9.9%
2 412
9.5%
3 427
9.8%
4 423
9.7%
5 423
9.7%
6 459
10.6%
7 433
10.0%
8 429
9.9%
9 457
10.5%
10 453
10.4%
ValueCountFrequency (%)
10 453
10.4%
9 457
10.5%
8 429
9.9%
7 433
10.0%
6 459
10.6%
5 423
9.7%
4 423
9.7%
3 427
9.8%
2 412
9.5%
1 430
9.9%

Driving_License
Text

Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size292.8 KiB
2025-03-22T22:27:08.756570image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters52152
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4346 ?
Unique (%)100.0%

Sample

1st rowDL8020609543
2nd rowDL7077290714
3rd rowDL0670616572
4th rowDL1103018486
5th rowDL4565815686
ValueCountFrequency (%)
dl8020609543 1
 
< 0.1%
dl3744485240 1
 
< 0.1%
dl4866458509 1
 
< 0.1%
dl5469750715 1
 
< 0.1%
dl0670616572 1
 
< 0.1%
dl1103018486 1
 
< 0.1%
dl4565815686 1
 
< 0.1%
dl0540514387 1
 
< 0.1%
dl2976563578 1
 
< 0.1%
dl9457306658 1
 
< 0.1%
Other values (4336) 4336
99.8%
2025-03-22T22:27:09.451254image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 4403
8.4%
6 4390
8.4%
7 4387
8.4%
3 4380
8.4%
8 4349
8.3%
D 4346
8.3%
L 4346
8.3%
1 4344
8.3%
4 4340
8.3%
0 4304
8.3%
Other values (2) 8563
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 4403
8.4%
6 4390
8.4%
7 4387
8.4%
3 4380
8.4%
8 4349
8.3%
D 4346
8.3%
L 4346
8.3%
1 4344
8.3%
4 4340
8.3%
0 4304
8.3%
Other values (2) 8563
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 4403
8.4%
6 4390
8.4%
7 4387
8.4%
3 4380
8.4%
8 4349
8.3%
D 4346
8.3%
L 4346
8.3%
1 4344
8.3%
4 4340
8.3%
0 4304
8.3%
Other values (2) 8563
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 4403
8.4%
6 4390
8.4%
7 4387
8.4%
3 4380
8.4%
8 4349
8.3%
D 4346
8.3%
L 4346
8.3%
1 4344
8.3%
4 4340
8.3%
0 4304
8.3%
Other values (2) 8563
16.4%

RC_Book
Text

Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size292.8 KiB
2025-03-22T22:27:09.953002image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters52152
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4346 ?
Unique (%)100.0%

Sample

1st rowRC7497152834
2nd rowRC9661080351
3rd rowRC4001294311
4th rowRC3296984930
5th rowRC5883571271
ValueCountFrequency (%)
rc7497152834 1
 
< 0.1%
rc4061602024 1
 
< 0.1%
rc9481263277 1
 
< 0.1%
rc9313024839 1
 
< 0.1%
rc4001294311 1
 
< 0.1%
rc3296984930 1
 
< 0.1%
rc5883571271 1
 
< 0.1%
rc4037615510 1
 
< 0.1%
rc7713712301 1
 
< 0.1%
rc7088645582 1
 
< 0.1%
Other values (4336) 4336
99.8%
2025-03-22T22:27:10.610032image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4479
8.6%
2 4436
8.5%
4 4402
8.4%
9 4391
8.4%
5 4373
8.4%
R 4346
8.3%
C 4346
8.3%
8 4335
8.3%
6 4290
8.2%
1 4256
8.2%
Other values (2) 8498
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4479
8.6%
2 4436
8.5%
4 4402
8.4%
9 4391
8.4%
5 4373
8.4%
R 4346
8.3%
C 4346
8.3%
8 4335
8.3%
6 4290
8.2%
1 4256
8.2%
Other values (2) 8498
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4479
8.6%
2 4436
8.5%
4 4402
8.4%
9 4391
8.4%
5 4373
8.4%
R 4346
8.3%
C 4346
8.3%
8 4335
8.3%
6 4290
8.2%
1 4256
8.2%
Other values (2) 8498
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4479
8.6%
2 4436
8.5%
4 4402
8.4%
9 4391
8.4%
5 4373
8.4%
R 4346
8.3%
C 4346
8.3%
8 4335
8.3%
6 4290
8.2%
1 4256
8.2%
Other values (2) 8498
16.3%

Claim_amount
Real number (ℝ)

High correlation 

Distinct4335
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2508309 × 10-17
Minimum-1.461847
Maximum3.2101218
Zeros0
Zeros (%)0.0%
Negative2500
Negative (%)57.5%
Memory size67.9 KiB
2025-03-22T22:27:10.847872image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.461847
5-th percentile-1.238291
Q1-0.80493016
median-0.22779828
Q30.66254566
95-th percentile1.9667393
Maximum3.2101218
Range4.6719688
Interquartile range (IQR)1.4674758

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)2.3527519 × 1016
Kurtosis-0.22288962
Mean4.2508309 × 10-17
Median Absolute Deviation (MAD)0.68856063
Skewness0.74867897
Sum1.438849 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:11.173196image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.178046434 2
 
< 0.1%
-0.7063579229 2
 
< 0.1%
-1.040913633 2
 
< 0.1%
-1.0076033 2
 
< 0.1%
1.414190436 2
 
< 0.1%
-0.06743714709 2
 
< 0.1%
0.9863501636 2
 
< 0.1%
-0.6577292006 2
 
< 0.1%
1.711765783 2
 
< 0.1%
-0.1484253158 2
 
< 0.1%
Other values (4325) 4326
99.5%
ValueCountFrequency (%)
-1.461846953 1
< 0.1%
-1.457239062 1
< 0.1%
-1.455705269 1
< 0.1%
-1.450954093 1
< 0.1%
-1.449589637 1
< 0.1%
-1.447736711 1
< 0.1%
-1.446717439 1
< 0.1%
-1.442676171 1
< 0.1%
-1.442558939 1
< 0.1%
-1.442431937 1
< 0.1%
ValueCountFrequency (%)
3.210121803 1
< 0.1%
3.084780644 1
< 0.1%
3.059223944 1
< 0.1%
3.030124214 1
< 0.1%
3.005814738 1
< 0.1%
3.00282205 1
< 0.1%
2.959986571 1
< 0.1%
2.957462814 1
< 0.1%
2.953349905 1
< 0.1%
2.928845041 1
< 0.1%

Deductibles
Real number (ℝ)

High correlation 

Distinct4185
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8857659 × 10-17
Minimum-1.232624
Maximum2.955744
Zeros0
Zeros (%)0.0%
Negative2652
Negative (%)61.0%
Memory size67.9 KiB
2025-03-22T22:27:11.509737image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1.232624
5-th percentile-1.0965646
Q1-0.78859487
median-0.31255027
Q30.57646032
95-th percentile2.1423625
Maximum2.955744
Range4.188368
Interquartile range (IQR)1.3650552

Descriptive statistics

Standard deviation1.0001151
Coefficient of variation (CV)1.6992097 × 1016
Kurtosis0.22575573
Mean5.8857659 × 10-17
Median Absolute Deviation (MAD)0.58324563
Skewness1.023832
Sum2.3925306 × 10-13
Variance1.0002301
MonotonicityNot monotonic
2025-03-22T22:27:11.857557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8003756306 3
 
0.1%
-0.8894032375 3
 
0.1%
-0.9366828459 3
 
0.1%
0.2258423592 3
 
0.1%
-0.6231031917 3
 
0.1%
-0.6338011163 3
 
0.1%
-1.094281541 3
 
0.1%
0.8344238083 2
 
< 0.1%
0.4515424765 2
 
< 0.1%
-0.8537608838 2
 
< 0.1%
Other values (4175) 4319
99.4%
ValueCountFrequency (%)
-1.232623971 1
< 0.1%
-1.231528086 1
< 0.1%
-1.230954051 1
< 0.1%
-1.224483111 1
< 0.1%
-1.224222186 1
< 0.1%
-1.223648151 1
< 0.1%
-1.223335041 1
< 0.1%
-1.223230671 1
< 0.1%
-1.221612936 1
< 0.1%
-1.220099571 1
< 0.1%
ValueCountFrequency (%)
2.955743986 1
< 0.1%
2.951830111 1
< 0.1%
2.951673556 1
< 0.1%
2.945776651 1
< 0.1%
2.943689251 1
< 0.1%
2.942384626 1
< 0.1%
2.940558151 1
< 0.1%
2.934139396 1
< 0.1%
2.923076177 1
< 0.1%
2.921301887 1
< 0.1%

Previous_claims
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4779107
Minimum0
Maximum5
Zeros742
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:27:12.053945image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7111547
Coefficient of variation (CV)0.69056351
Kurtosis-1.273455
Mean2.4779107
Median Absolute Deviation (MAD)2
Skewness0.016550199
Sum10769
Variance2.9280505
MonotonicityNot monotonic
2025-03-22T22:27:12.292448image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 742
17.1%
1 732
16.8%
2 726
16.7%
4 719
16.5%
5 714
16.4%
3 713
16.4%
ValueCountFrequency (%)
0 742
17.1%
1 732
16.8%
2 726
16.7%
3 713
16.4%
4 719
16.5%
5 714
16.4%
ValueCountFrequency (%)
5 714
16.4%
4 719
16.5%
3 713
16.4%
2 726
16.7%
1 732
16.8%
0 742
17.1%

Claim_status
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
2
1481 
0
1471 
1
1394 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
2 1481
34.1%
0 1471
33.8%
1 1394
32.1%

Length

2025-03-22T22:27:12.567436image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:12.811963image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1481
34.1%
0 1471
33.8%
1 1394
32.1%

Most occurring characters

ValueCountFrequency (%)
2 1481
34.1%
0 1471
33.8%
1 1394
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1481
34.1%
0 1471
33.8%
1 1394
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1481
34.1%
0 1471
33.8%
1 1394
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1481
34.1%
0 1471
33.8%
1 1394
32.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
False
2177 
True
2169 
ValueCountFrequency (%)
False 2177
50.1%
True 2169
49.9%
2025-03-22T22:27:12.979624image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Loss_of_wages
Real number (ℝ)

Distinct4112
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29916.354
Minimum10002
Maximum49992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:27:13.224414image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum10002
5-th percentile12088.25
Q120139.25
median29617
Q339988.5
95-th percentile48051.75
Maximum49992
Range39990
Interquartile range (IQR)19849.25

Descriptive statistics

Standard deviation11514.759
Coefficient of variation (CV)0.38489849
Kurtosis-1.1914904
Mean29916.354
Median Absolute Deviation (MAD)9868.5
Skewness0.032121791
Sum1.3001647 × 108
Variance1.3258968 × 108
MonotonicityNot monotonic
2025-03-22T22:27:13.604622image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48232 3
 
0.1%
47508 3
 
0.1%
28385 3
 
0.1%
31521 3
 
0.1%
42312 3
 
0.1%
20022 3
 
0.1%
28435 3
 
0.1%
26496 3
 
0.1%
48283 3
 
0.1%
24601 3
 
0.1%
Other values (4102) 4316
99.3%
ValueCountFrequency (%)
10002 1
< 0.1%
10007 1
< 0.1%
10013 1
< 0.1%
10015 1
< 0.1%
10055 1
< 0.1%
10072 1
< 0.1%
10081 1
< 0.1%
10085 1
< 0.1%
10096 1
< 0.1%
10116 1
< 0.1%
ValueCountFrequency (%)
49992 1
< 0.1%
49985 1
< 0.1%
49983 1
< 0.1%
49982 2
< 0.1%
49977 1
< 0.1%
49976 1
< 0.1%
49953 1
< 0.1%
49952 1
< 0.1%
49942 1
< 0.1%
49924 1
< 0.1%

Red_flags
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
False
2210 
True
2136 
ValueCountFrequency (%)
False 2210
50.9%
True 2136
49.1%
2025-03-22T22:27:13.955239image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Investigation
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size278.9 KiB
Under process
1486 
Closed
1431 
Pending
1429 

Length

Max length13
Median length7
Mean length8.7222734
Min length6

Characters and Unicode

Total characters37907
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed
2nd rowUnder process
3rd rowUnder process
4th rowPending
5th rowClosed

Common Values

ValueCountFrequency (%)
Under process 1486
34.2%
Closed 1431
32.9%
Pending 1429
32.9%

Length

2025-03-22T22:27:14.205877image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:14.454453image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
under 1486
25.5%
process 1486
25.5%
closed 1431
24.5%
pending 1429
24.5%

Most occurring characters

ValueCountFrequency (%)
e 5832
15.4%
s 4403
11.6%
d 4346
11.5%
n 4344
11.5%
r 2972
7.8%
o 2917
7.7%
U 1486
 
3.9%
1486
 
3.9%
p 1486
 
3.9%
c 1486
 
3.9%
Other values (5) 7149
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5832
15.4%
s 4403
11.6%
d 4346
11.5%
n 4344
11.5%
r 2972
7.8%
o 2917
7.7%
U 1486
 
3.9%
1486
 
3.9%
p 1486
 
3.9%
c 1486
 
3.9%
Other values (5) 7149
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5832
15.4%
s 4403
11.6%
d 4346
11.5%
n 4344
11.5%
r 2972
7.8%
o 2917
7.7%
U 1486
 
3.9%
1486
 
3.9%
p 1486
 
3.9%
c 1486
 
3.9%
Other values (5) 7149
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5832
15.4%
s 4403
11.6%
d 4346
11.5%
n 4344
11.5%
r 2972
7.8%
o 2917
7.7%
U 1486
 
3.9%
1486
 
3.9%
p 1486
 
3.9%
c 1486
 
3.9%
Other values (5) 7149
18.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size288.5 KiB
Compliant
2207 
Non-compliant
2139 

Length

Max length13
Median length9
Mean length10.968707
Min length9

Characters and Unicode

Total characters47670
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompliant
2nd rowNon-compliant
3rd rowNon-compliant
4th rowCompliant
5th rowCompliant

Common Values

ValueCountFrequency (%)
Compliant 2207
50.8%
Non-compliant 2139
49.2%

Length

2025-03-22T22:27:14.730787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:14.945664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
compliant 2207
50.8%
non-compliant 2139
49.2%

Most occurring characters

ValueCountFrequency (%)
o 6485
13.6%
n 6485
13.6%
m 4346
9.1%
p 4346
9.1%
l 4346
9.1%
i 4346
9.1%
a 4346
9.1%
t 4346
9.1%
C 2207
 
4.6%
N 2139
 
4.5%
Other values (2) 4278
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6485
13.6%
n 6485
13.6%
m 4346
9.1%
p 4346
9.1%
l 4346
9.1%
i 4346
9.1%
a 4346
9.1%
t 4346
9.1%
C 2207
 
4.6%
N 2139
 
4.5%
Other values (2) 4278
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6485
13.6%
n 6485
13.6%
m 4346
9.1%
p 4346
9.1%
l 4346
9.1%
i 4346
9.1%
a 4346
9.1%
t 4346
9.1%
C 2207
 
4.6%
N 2139
 
4.5%
Other values (2) 4278
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6485
13.6%
n 6485
13.6%
m 4346
9.1%
p 4346
9.1%
l 4346
9.1%
i 4346
9.1%
a 4346
9.1%
t 4346
9.1%
C 2207
 
4.6%
N 2139
 
4.5%
Other values (2) 4278
9.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
False
2215 
True
2131 
ValueCountFrequency (%)
False 2215
51.0%
True 2131
49.0%
2025-03-22T22:27:15.224154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Fraud_detection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size267.4 KiB
Passed
2183 
Failed
2163 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters26076
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFailed
2nd rowPassed
3rd rowPassed
4th rowFailed
5th rowFailed

Common Values

ValueCountFrequency (%)
Passed 2183
50.2%
Failed 2163
49.8%

Length

2025-03-22T22:27:15.477154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:15.688003image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
passed 2183
50.2%
failed 2163
49.8%

Most occurring characters

ValueCountFrequency (%)
s 4366
16.7%
a 4346
16.7%
e 4346
16.7%
d 4346
16.7%
P 2183
8.4%
F 2163
8.3%
i 2163
8.3%
l 2163
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 4366
16.7%
a 4346
16.7%
e 4346
16.7%
d 4346
16.7%
P 2183
8.4%
F 2163
8.3%
i 2163
8.3%
l 2163
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 4366
16.7%
a 4346
16.7%
e 4346
16.7%
d 4346
16.7%
P 2183
8.4%
F 2163
8.3%
i 2163
8.3%
l 2163
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 4366
16.7%
a 4346
16.7%
e 4346
16.7%
d 4346
16.7%
P 2183
8.4%
F 2163
8.3%
i 2163
8.3%
l 2163
8.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.9 KiB
Approved
2205 
Rejected
2141 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters34768
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowRejected
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Approved 2205
50.7%
Rejected 2141
49.3%

Length

2025-03-22T22:27:15.930108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:16.175496image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
approved 2205
50.7%
rejected 2141
49.3%

Most occurring characters

ValueCountFrequency (%)
e 8628
24.8%
p 4410
12.7%
d 4346
12.5%
A 2205
 
6.3%
r 2205
 
6.3%
o 2205
 
6.3%
v 2205
 
6.3%
R 2141
 
6.2%
j 2141
 
6.2%
c 2141
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8628
24.8%
p 4410
12.7%
d 4346
12.5%
A 2205
 
6.3%
r 2205
 
6.3%
o 2205
 
6.3%
v 2205
 
6.3%
R 2141
 
6.2%
j 2141
 
6.2%
c 2141
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8628
24.8%
p 4410
12.7%
d 4346
12.5%
A 2205
 
6.3%
r 2205
 
6.3%
o 2205
 
6.3%
v 2205
 
6.3%
R 2141
 
6.2%
j 2141
 
6.2%
c 2141
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8628
24.8%
p 4410
12.7%
d 4346
12.5%
A 2205
 
6.3%
r 2205
 
6.3%
o 2205
 
6.3%
v 2205
 
6.3%
R 2141
 
6.2%
j 2141
 
6.2%
c 2141
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
False
2182 
True
2164 
ValueCountFrequency (%)
False 2182
50.2%
True 2164
49.8%
2025-03-22T22:27:16.424482image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
2205 
1
2141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2205
50.7%
1 2141
49.3%

Length

2025-03-22T22:27:16.670726image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:16.928039image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2205
50.7%
1 2141
49.3%

Most occurring characters

ValueCountFrequency (%)
0 2205
50.7%
1 2141
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2205
50.7%
1 2141
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2205
50.7%
1 2141
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2205
50.7%
1 2141
49.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
2189 
1
2157 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2189
50.4%
1 2157
49.6%

Length

2025-03-22T22:27:17.233282image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:17.532423image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2189
50.4%
1 2157
49.6%

Most occurring characters

ValueCountFrequency (%)
0 2189
50.4%
1 2157
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2189
50.4%
1 2157
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2189
50.4%
1 2157
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2189
50.4%
1 2157
49.6%

Driver_license
Text

Unique 

Distinct4346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size292.8 KiB
2025-03-22T22:27:18.064391image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters52152
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4346 ?
Unique (%)100.0%

Sample

1st rowDL1927770604
2nd rowDL8476940969
3rd rowDL3072939753
4th rowDL8035241735
5th rowDL1710473124
ValueCountFrequency (%)
dl1927770604 1
 
< 0.1%
dl1502527369 1
 
< 0.1%
dl1093031794 1
 
< 0.1%
dl7847987089 1
 
< 0.1%
dl3072939753 1
 
< 0.1%
dl8035241735 1
 
< 0.1%
dl1710473124 1
 
< 0.1%
dl1524982531 1
 
< 0.1%
dl3617075167 1
 
< 0.1%
dl7053579378 1
 
< 0.1%
Other values (4336) 4336
99.8%
2025-03-22T22:27:18.786296image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 4459
8.6%
7 4409
8.5%
2 4387
8.4%
0 4370
8.4%
1 4357
8.4%
D 4346
8.3%
L 4346
8.3%
9 4342
8.3%
4 4335
8.3%
8 4312
8.3%
Other values (2) 8489
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 4459
8.6%
7 4409
8.5%
2 4387
8.4%
0 4370
8.4%
1 4357
8.4%
D 4346
8.3%
L 4346
8.3%
9 4342
8.3%
4 4335
8.3%
8 4312
8.3%
Other values (2) 8489
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 4459
8.6%
7 4409
8.5%
2 4387
8.4%
0 4370
8.4%
1 4357
8.4%
D 4346
8.3%
L 4346
8.3%
9 4342
8.3%
4 4335
8.3%
8 4312
8.3%
Other values (2) 8489
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 4459
8.6%
7 4409
8.5%
2 4387
8.4%
0 4370
8.4%
1 4357
8.4%
D 4346
8.3%
L 4346
8.3%
9 4342
8.3%
4 4335
8.3%
8 4312
8.3%
Other values (2) 8489
16.3%

Driver_history
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
2
1115 
1
1106 
3
1071 
0
1054 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row3
5th row0

Common Values

ValueCountFrequency (%)
2 1115
25.7%
1 1106
25.4%
3 1071
24.6%
0 1054
24.3%

Length

2025-03-22T22:27:18.984042image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:19.162415image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1115
25.7%
1 1106
25.4%
3 1071
24.6%
0 1054
24.3%

Most occurring characters

ValueCountFrequency (%)
2 1115
25.7%
1 1106
25.4%
3 1071
24.6%
0 1054
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1115
25.7%
1 1106
25.4%
3 1071
24.6%
0 1054
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1115
25.7%
1 1106
25.4%
3 1071
24.6%
0 1054
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1115
25.7%
1 1106
25.4%
3 1071
24.6%
0 1054
24.3%

Driving_experience
Real number (ℝ)

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.530143
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:27:19.436707image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7619951
Coefficient of variation (CV)0.5471906
Kurtosis-1.2004119
Mean10.530143
Median Absolute Deviation (MAD)5
Skewness-0.012913939
Sum45764
Variance33.200587
MonotonicityNot monotonic
2025-03-22T22:27:19.706788image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
19 250
 
5.8%
3 234
 
5.4%
6 231
 
5.3%
12 230
 
5.3%
8 230
 
5.3%
17 227
 
5.2%
16 226
 
5.2%
7 221
 
5.1%
2 220
 
5.1%
11 219
 
5.0%
Other values (10) 2058
47.4%
ValueCountFrequency (%)
1 217
5.0%
2 220
5.1%
3 234
5.4%
4 197
4.5%
5 181
4.2%
6 231
5.3%
7 221
5.1%
8 230
5.3%
9 212
4.9%
10 216
5.0%
ValueCountFrequency (%)
20 201
4.6%
19 250
5.8%
18 197
4.5%
17 227
5.2%
16 226
5.2%
15 211
4.9%
14 208
4.8%
13 218
5.0%
12 230
5.3%
11 219
5.0%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size298.2 KiB
No violations
1095 
1 parking violation
1092 
1 speeding
1082 
2 accidents
1077 

Length

Max length19
Median length13
Mean length13.265071
Min length10

Characters and Unicode

Total characters57650
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 parking violation
2nd row1 speeding
3rd row1 speeding
4th row1 parking violation
5th row1 parking violation

Common Values

ValueCountFrequency (%)
No violations 1095
25.2%
1 parking violation 1092
25.1%
1 speeding 1082
24.9%
2 accidents 1077
24.8%

Length

2025-03-22T22:27:19.979601image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:20.152643image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2174
22.2%
no 1095
11.2%
violations 1095
11.2%
parking 1092
11.2%
violation 1092
11.2%
speeding 1082
11.1%
2 1077
11.0%
accidents 1077
11.0%

Most occurring characters

ValueCountFrequency (%)
i 7625
13.2%
o 5469
 
9.5%
5438
 
9.4%
n 5438
 
9.4%
a 4356
 
7.6%
t 3264
 
5.7%
s 3254
 
5.6%
e 3241
 
5.6%
v 2187
 
3.8%
l 2187
 
3.8%
Other values (9) 15191
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7625
13.2%
o 5469
 
9.5%
5438
 
9.4%
n 5438
 
9.4%
a 4356
 
7.6%
t 3264
 
5.7%
s 3254
 
5.6%
e 3241
 
5.6%
v 2187
 
3.8%
l 2187
 
3.8%
Other values (9) 15191
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7625
13.2%
o 5469
 
9.5%
5438
 
9.4%
n 5438
 
9.4%
a 4356
 
7.6%
t 3264
 
5.7%
s 3254
 
5.6%
e 3241
 
5.6%
v 2187
 
3.8%
l 2187
 
3.8%
Other values (9) 15191
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7625
13.2%
o 5469
 
9.5%
5438
 
9.4%
n 5438
 
9.4%
a 4356
 
7.6%
t 3264
 
5.7%
s 3254
 
5.6%
e 3241
 
5.6%
v 2187
 
3.8%
l 2187
 
3.8%
Other values (9) 15191
26.4%
Distinct1645
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2025-03-19 00:00:00
Maximum2030-03-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-22T22:27:20.427987image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:27:20.659544image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
2177 
1
2169 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2177
50.1%
1 2169
49.9%

Length

2025-03-22T22:27:21.006311image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:21.244399image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2177
50.1%
1 2169
49.9%

Most occurring characters

ValueCountFrequency (%)
0 2177
50.1%
1 2169
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2177
50.1%
1 2169
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2177
50.1%
1 2169
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2177
50.1%
1 2169
49.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size246.2 KiB
0
2202 
1
2144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2202
50.7%
1 2144
49.3%

Length

2025-03-22T22:27:21.514516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:21.772083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2202
50.7%
1 2144
49.3%

Most occurring characters

ValueCountFrequency (%)
0 2202
50.7%
1 2144
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2202
50.7%
1 2144
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2202
50.7%
1 2144
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2202
50.7%
1 2144
49.3%

License_Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size278.0 KiB
Learning
2186 
Permanent
2160 

Length

Max length9
Median length8
Mean length8.4970087
Min length8

Characters and Unicode

Total characters36928
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPermanent
2nd rowLearning
3rd rowLearning
4th rowLearning
5th rowLearning

Common Values

ValueCountFrequency (%)
Learning 2186
50.3%
Permanent 2160
49.7%

Length

2025-03-22T22:27:22.015654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-22T22:27:22.230221image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
learning 2186
50.3%
permanent 2160
49.7%

Most occurring characters

ValueCountFrequency (%)
n 8692
23.5%
e 6506
17.6%
a 4346
11.8%
r 4346
11.8%
L 2186
 
5.9%
i 2186
 
5.9%
g 2186
 
5.9%
P 2160
 
5.8%
m 2160
 
5.8%
t 2160
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8692
23.5%
e 6506
17.6%
a 4346
11.8%
r 4346
11.8%
L 2186
 
5.9%
i 2186
 
5.9%
g 2186
 
5.9%
P 2160
 
5.8%
m 2160
 
5.8%
t 2160
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8692
23.5%
e 6506
17.6%
a 4346
11.8%
r 4346
11.8%
L 2186
 
5.9%
i 2186
 
5.9%
g 2186
 
5.9%
P 2160
 
5.8%
m 2160
 
5.8%
t 2160
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8692
23.5%
e 6506
17.6%
a 4346
11.8%
r 4346
11.8%
L 2186
 
5.9%
i 2186
 
5.9%
g 2186
 
5.9%
P 2160
 
5.8%
m 2160
 
5.8%
t 2160
 
5.8%

Number_of_days
Real number (ℝ)

Distinct657
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.32812
Minimum11
Maximum729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.9 KiB
2025-03-22T22:27:22.491884image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile117
Q1258.25
median363
Q3471
95-th percentile618
Maximum729
Range718
Interquartile range (IQR)212.75

Descriptive statistics

Standard deviation149.57905
Coefficient of variation (CV)0.41169137
Kurtosis-0.59269732
Mean363.32812
Median Absolute Deviation (MAD)106
Skewness0.025418386
Sum1579024
Variance22373.892
MonotonicityNot monotonic
2025-03-22T22:27:22.847978image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415 19
 
0.4%
369 18
 
0.4%
374 18
 
0.4%
312 18
 
0.4%
289 18
 
0.4%
383 17
 
0.4%
390 17
 
0.4%
259 16
 
0.4%
288 16
 
0.4%
428 16
 
0.4%
Other values (647) 4173
96.0%
ValueCountFrequency (%)
11 1
 
< 0.1%
13 1
 
< 0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
21 2
< 0.1%
22 1
 
< 0.1%
23 3
0.1%
26 3
0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
729 1
 
< 0.1%
724 1
 
< 0.1%
717 2
< 0.1%
711 1
 
< 0.1%
709 1
 
< 0.1%
708 2
< 0.1%
704 4
0.1%
703 2
< 0.1%
702 1
 
< 0.1%
701 1
 
< 0.1%

Interactions

2025-03-22T22:26:39.626294image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:38.934160image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:42.895301image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:46.832916image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:50.352385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:53.500114image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:56.616131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:00.594559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:03.979534image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:07.273888image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:10.668297image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:13.824630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-03-22T22:26:30.146341image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:33.978845image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:38.251952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:42.159858image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:41.746428image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:45.659026image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:49.267895image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:52.433071image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:55.644370image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:59.411288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:02.808411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:06.166850image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:09.548782image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:12.640673image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:16.648964image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:19.986489image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:23.439288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:26.688061image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:30.316069image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:34.201777image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:38.401882image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:42.373423image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:41.939923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:45.825043image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-03-22T22:26:06.304638image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:09.692955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:12.764584image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:16.801183image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:20.124190image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:23.611049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:26.846647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:30.549788image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:34.368205image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:38.561960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:42.541712image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:42.112882image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:46.010529image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:49.603009image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:52.769994image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:55.904088image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:59.824341image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:03.150025image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:06.488380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:09.838099image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:12.930710image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:17.017481image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:20.273081image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:23.782243image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:27.021109image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:30.740757image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:34.564773image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:38.753221image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:42.691361image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:42.332680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:46.208211image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:49.766173image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:52.932915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:56.092741image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:59.990654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:03.333091image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:06.621976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:10.035582image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:13.141867image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:17.180867image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:20.454780image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:23.934465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:27.197671image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:30.944502image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:34.768677image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:38.959748image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:42.912577image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:42.528103image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:46.464579image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:50.022253image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:53.129147image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:56.271766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:00.225465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:03.560704image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-03-22T22:26:10.237868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:13.378880image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:17.411779image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:20.684862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:24.127052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:27.427464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:31.186232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:34.999136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:39.234149image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:43.126300image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:42.743131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:46.637206image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:50.194685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:53.290248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:25:56.456559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:00.414128image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:03.761275image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:07.052821image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:10.450729image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:13.602957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:17.640464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:20.912817image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:24.294063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:27.637742image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:31.452352image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:35.176748image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-22T22:26:39.443557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-03-22T22:27:23.262930image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Alcohol_drug_test_resultsCause_of_incidentClaim_amountClaim_statusClaim_verificationClaimant_ageClaimant_driver_historyClaims_approval_statusComprehensiveCourt_casesCoverageCoverage_Add_OnsDamage_to_property_outside_the_vehicleDeductiblesDepreciationDisability_statusDriver_historyDriving_experienceExtent_of_damageFinal_settlement_approvalFraud_detectionFraud_detection_approvalFuel_typeIncident_descriptionIncident_severity_levelInsurance_coverage_typeInsurance_settlement_amountInsured_amountInvestigationInvolvement_of_pedestrians_or_animalsLicense_TypeLicense_class_if_applicableLoss_of_wagesNature_of_incidentNumber_of_daysOdometer_readingOwn_DamageParts_replacedPayment_FrequencyPayment_statusPolicy_StatusPremium_amountPrevious_claimsPrevious_traffic_violationsRed_flagsRegulatory_complianceRepair_CostRepair_durationRepair_estimatesSalvage_valueThird_PartyTraffic_condition_at_the_time_of_incidentVehicle_conditionVehicle_history_accident_repair_historyVehicle_safety_featuresVehicle_type_sedan_SUV_etcVehicle_value_market_valueWeather_conditions_at_the_time_of_the_accident
Alcohol_drug_test_results1.0000.0200.0000.0140.0000.0230.0000.0260.0000.0150.0000.0000.0190.0000.0000.0240.0000.0000.0400.0000.0000.0000.0000.0230.0000.0000.0000.0340.0000.0000.0000.0100.0000.0000.0150.0000.0000.0000.0260.0180.0130.0000.0310.0000.0110.0000.0000.0420.0000.0220.0070.0000.0000.0000.0000.0000.0540.000
Cause_of_incident0.0201.0000.0000.0070.0000.0000.0320.0000.0080.0000.0140.0000.0000.0130.0000.0000.0310.0300.0000.0150.0000.0200.0000.0000.0000.0250.0000.0250.0000.0000.0000.0000.0210.0190.0000.0000.0000.0000.0000.0210.0140.0000.0200.0070.0280.0000.0000.0080.0000.0280.0000.0000.0000.0330.0000.0000.0210.002
Claim_amount0.0000.0001.0000.0000.0270.0180.0260.0000.0000.0130.0000.0000.0570.7630.4870.0000.000-0.0010.0020.0270.0000.0000.0000.0000.0000.0000.9990.6530.0000.0000.0000.035-0.0010.0100.004-0.0020.0400.0000.0000.0000.0190.5250.0190.0070.0000.0400.964-0.0040.9640.4070.0000.0000.0070.0320.0000.0000.5680.022
Claim_status0.0140.0070.0001.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0340.0070.0220.0240.0170.0430.0000.0140.0000.0000.0070.0000.0250.0000.0000.0250.0110.0290.0370.0190.0000.0000.0000.0000.0240.0000.0070.0210.0220.0000.0190.0000.0140.0000.0000.0000.0000.0270.0000.0230.009
Claim_verification0.0000.0000.0270.0001.0000.0000.0000.0150.0000.0000.0000.0080.0100.0000.0000.0260.0000.0000.0000.0000.0150.0000.0040.0000.0390.0000.0320.0000.0440.0150.0000.0230.0000.0500.0000.0000.0000.0150.0150.0170.0340.0000.0000.0000.0000.0060.0160.0000.0160.0000.0120.0000.0000.0000.0320.0310.0000.012
Claimant_age0.0230.0000.0180.0000.0001.0000.0170.0000.0220.0000.0000.0000.0430.0160.0050.0000.015-0.0040.0130.0000.0000.0320.0000.0260.0370.0360.0180.0150.0000.0070.0000.000-0.0010.014-0.001-0.0450.0000.0000.0060.0000.0260.019-0.0110.0360.0170.0290.0190.0030.019-0.0030.0270.0240.0120.0100.0200.0130.0170.023
Claimant_driver_history0.0000.0320.0260.0000.0000.0171.0000.0000.0000.0260.0000.0240.0000.0350.0210.0130.0000.0230.0000.0000.0160.0180.0260.0000.0000.0000.0200.0000.0140.0180.0250.0000.0000.0000.0000.0210.0000.0000.0350.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0110.0310.0000.0000.0020.0000.0000.011
Claims_approval_status0.0260.0000.0000.0000.0150.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0070.0130.0130.0230.0000.0000.0000.0000.0110.0250.0220.0000.0110.0000.0000.0370.0000.0390.0000.0240.0370.0100.0000.0330.0270.0000.0210.0000.0000.0300.0000.0000.0100.0000.0000.0320.0000.0320.0000.000
Comprehensive0.0000.0080.0000.0000.0000.0220.0000.0001.0000.0000.0200.0280.0000.0000.0000.0160.0000.0000.0250.0140.0260.0000.0490.0000.0040.0110.0000.0000.0000.0000.0000.0100.0210.0000.0000.0000.0000.0000.0380.0000.0250.0210.0240.0410.0160.0000.0000.0170.0000.0000.0000.0000.0080.0070.0000.0000.0000.025
Court_cases0.0150.0000.0130.0000.0000.0000.0260.0000.0001.0000.0000.0000.0000.0150.0190.0100.0000.0000.0270.0000.0030.0000.0000.0200.0000.0140.0180.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0070.0000.0000.0170.0150.0000.0210.0000.0210.0000.0120.0000.0000.0000.0000.0200.0080.000
Coverage0.0000.0140.0000.0000.0000.0000.0000.0000.0200.0001.0000.0000.0200.0140.0030.0420.0000.0000.0230.0000.0180.0100.0170.0000.0070.0000.0070.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0190.0000.0200.0050.0000.0000.0000.0070.0000.0000.0000.0000.0270.0230.0120.0000.0000.0000.0140.0000.000
Coverage_Add_Ons0.0000.0000.0000.0220.0080.0000.0240.0000.0280.0000.0001.0000.0240.0000.0110.0120.0070.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0280.0200.0000.0000.0000.0240.0000.0150.0340.0000.0000.0000.0290.0190.0000.0000.0140.0000.0000.0000.0000.0000.0310.0000.0120.0000.0100.0000.0340.0000.000
Damage_to_property_outside_the_vehicle0.0190.0000.0570.0000.0100.0430.0000.0000.0000.0000.0200.0241.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0150.0360.0000.0040.0560.0000.0190.0210.0250.0000.0000.0000.0630.0180.0000.0000.0000.0250.0000.0000.0070.0000.0130.0160.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.000
Deductibles0.0000.0130.7630.0000.0000.0160.0350.0000.0000.0150.0140.0000.0341.0000.3800.0510.0000.0210.0010.0000.0000.0000.0150.0210.0230.0000.7400.5000.0000.0380.0000.008-0.0040.0250.014-0.0080.0000.0180.0000.0390.0210.4040.0140.0000.0000.0490.738-0.0060.7380.3140.0220.0000.0000.0000.0000.0310.4320.017
Depreciation0.0000.0000.4870.0000.0000.0050.0210.0000.0000.0190.0030.0110.0000.3801.0000.0310.0000.0170.0020.0130.0000.0000.0230.0000.0270.0320.4870.7270.0160.0000.0160.023-0.0120.000-0.002-0.0210.0270.0260.0000.0320.0000.5720.0060.0000.0000.0000.470-0.0200.4700.6100.0000.0000.0000.0300.0110.0000.8350.000
Disability_status0.0240.0000.0000.0000.0260.0000.0130.0040.0160.0100.0420.0120.0000.0510.0311.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0540.0290.0000.0180.0000.0000.0000.0000.0100.0170.0000.0000.0000.0000.0000.0000.0100.0090.0000.0000.0000.0000.0000.000
Driver_history0.0000.0310.0000.0000.0000.0150.0000.0000.0000.0000.0000.0070.0000.0000.0000.0001.0000.0050.0000.0000.0260.0370.0000.0210.0000.0000.0260.0000.0120.0000.0230.0350.0000.0000.0000.0000.0100.0090.0000.0000.0000.0000.0210.0000.0000.0000.0000.0190.0000.0190.0180.0210.0000.0150.0140.0260.0000.000
Driving_experience0.0000.030-0.0010.0340.000-0.0040.0230.0000.0000.0000.0000.0000.0000.0210.0170.0000.0051.000-0.0090.0230.0000.0240.0070.0000.0180.000-0.002-0.0090.0000.0000.0260.000-0.0030.0070.019-0.0160.0110.0000.0170.0450.000-0.0140.0110.0240.0360.000-0.005-0.008-0.0050.0080.0160.0280.0000.0190.0000.025-0.0040.013
Extent_of_damage0.0400.0000.0020.0070.0000.0130.0000.0070.0250.0270.0230.0000.0000.0010.0020.0000.000-0.0091.0000.0000.0000.0000.0000.0140.0000.0130.0020.0040.0000.0260.0000.000-0.0050.012-0.0020.0130.0260.0000.0190.0290.027-0.0040.0110.0000.0000.000-0.0020.002-0.002-0.0000.0000.0000.0000.0140.0000.0000.0000.017
Final_settlement_approval0.0000.0150.0270.0220.0000.0000.0000.0130.0140.0000.0000.0000.0000.0000.0130.0000.0000.0230.0001.0000.0000.0240.0130.0100.0190.0000.0280.0000.0000.0000.0000.0260.0110.0120.0000.0000.0000.0130.0000.0000.0220.0000.0000.0140.0310.0080.0060.0290.0060.0300.0310.0000.0190.0000.0000.0250.0370.000
Fraud_detection0.0000.0000.0000.0240.0150.0000.0160.0130.0260.0030.0180.0000.0000.0000.0000.0000.0260.0000.0000.0001.0000.0150.0000.0100.0000.0200.0000.0000.0340.0000.0000.0250.0000.0310.0510.0410.0330.0000.0380.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0300.0000.0000.0120.0000.0000.0000.0000.028
Fraud_detection_approval0.0000.0200.0000.0170.0000.0320.0180.0230.0000.0000.0100.0000.0000.0000.0000.0000.0370.0240.0000.0240.0151.0000.0000.0140.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0360.0000.0000.0000.0200.0060.0150.0000.0160.0450.0050.0450.0000.0040.0000.0000.0000.0150.0000.0000.021
Fuel_type0.0000.0000.0000.0430.0040.0000.0260.0000.0490.0000.0170.0000.0150.0150.0230.0000.0000.0070.0000.0130.0000.0001.0000.0000.0000.0000.0000.0210.0120.0000.0000.0000.0000.0000.0310.0000.0000.0090.0000.0090.0000.0000.0260.0200.0000.0120.0290.0030.0290.0000.0000.0000.0000.0140.0230.0000.0000.016
Incident_description0.0230.0000.0000.0000.0000.0260.0000.0000.0000.0200.0000.0000.0360.0210.0000.0310.0210.0000.0140.0100.0100.0140.0001.0000.0200.0000.0000.0120.0090.0190.0240.0000.0000.0190.0000.0090.0290.0000.0130.0000.0040.0000.0000.0000.0000.0000.0080.0000.0080.0100.0360.0210.0000.0050.0000.0000.0000.000
Incident_severity_level0.0000.0000.0000.0140.0390.0370.0000.0000.0040.0000.0070.0000.0000.0230.0270.0000.0000.0180.0000.0190.0000.0000.0000.0201.0000.0000.0000.0170.0000.0310.0000.0000.0000.0050.0000.0370.0000.0000.0200.0000.0000.0230.0040.0000.0230.0180.0230.0120.0230.0340.0160.0000.0190.0260.0120.0000.0000.010
Insurance_coverage_type0.0000.0250.0000.0000.0000.0360.0000.0000.0110.0140.0000.0210.0040.0000.0320.0000.0000.0000.0130.0000.0200.0110.0000.0000.0001.0000.0000.0000.0000.0230.0000.0200.0000.0000.0000.0000.0000.0000.0000.0120.0000.0280.0000.0290.0190.0000.0170.0230.0170.0240.0450.0000.0150.0000.0060.0000.0150.010
Insurance_settlement_amount0.0000.0000.9990.0000.0320.0180.0200.0110.0000.0180.0070.0000.0560.7400.4870.0000.026-0.0020.0020.0280.0000.0000.0000.0000.0000.0001.0000.6530.0000.0000.0000.024-0.0010.0270.003-0.0020.0460.0000.0000.0000.0000.5250.0190.0140.0000.0380.964-0.0030.9640.4070.0000.0000.0000.0150.0000.0110.5680.013
Insured_amount0.0340.0250.6530.0070.0000.0150.0000.0250.0000.0000.0000.0280.0000.5000.7270.0000.000-0.0090.0040.0000.0000.0000.0210.0120.0170.0000.6531.0000.0240.0000.0000.000-0.0050.000-0.002-0.0170.0300.0110.0220.0190.0330.7760.0110.0000.0000.0000.630-0.0040.6300.6100.0000.0000.0000.0000.0200.0150.8510.000
Investigation0.0000.0000.0000.0000.0440.0000.0140.0220.0000.0000.0000.0200.0190.0000.0160.0030.0120.0000.0000.0000.0340.0000.0120.0090.0000.0000.0000.0241.0000.0000.0210.0000.0000.0290.0000.0330.0000.0000.0000.0230.0000.0000.0000.0000.0240.0000.0200.0000.0200.0080.0060.0140.0120.0060.0080.0190.0000.009
Involvement_of_pedestrians_or_animals0.0000.0000.0000.0250.0150.0070.0180.0000.0000.0000.0000.0000.0210.0380.0000.0000.0000.0000.0260.0000.0000.0000.0000.0190.0310.0230.0000.0000.0001.0000.0190.0100.0000.0250.0360.0000.0000.0190.0000.0000.0000.0000.0000.0170.0000.0000.0380.0370.0380.0000.0000.0050.0000.0130.0000.0000.0280.042
License_Type0.0000.0000.0000.0000.0000.0000.0250.0110.0000.0000.0000.0000.0250.0000.0160.0000.0230.0260.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0210.0191.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0170.0000.0000.0000.0310.0290.0000.0240.0200.0510.016
License_class_if_applicable0.0100.0000.0350.0000.0230.0000.0000.0000.0100.0000.0000.0000.0000.0080.0230.0000.0350.0000.0000.0260.0250.0000.0000.0000.0000.0200.0240.0000.0000.0100.0101.0000.0110.0260.0310.0000.0000.0000.0260.0000.0000.0340.0000.0000.0000.0030.0470.0300.0470.0310.0000.0000.0000.0200.0050.0030.0050.004
Loss_of_wages0.0000.021-0.0010.0250.000-0.0010.0000.0000.0210.0000.0340.0240.000-0.004-0.0120.0000.000-0.003-0.0050.0110.0000.0000.0000.0000.0000.000-0.001-0.0050.0000.0000.0000.0111.0000.0000.013-0.0030.0000.0000.0140.0000.018-0.008-0.0230.0000.0110.018-0.000-0.031-0.000-0.0240.0000.0000.0000.0340.0060.024-0.0090.025
Nature_of_incident0.0000.0190.0100.0110.0500.0140.0000.0370.0000.0000.0000.0000.0000.0250.0000.0000.0000.0070.0120.0120.0310.0000.0000.0190.0050.0000.0270.0000.0290.0250.0000.0260.0001.0000.0190.0000.0000.0000.0150.0000.0000.0240.0110.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0250.0000.023
Number_of_days0.0150.0000.0040.0290.000-0.0010.0000.0000.0000.0000.0000.0150.0630.014-0.0020.0540.0000.019-0.0020.0000.0510.0000.0310.0000.0000.0000.003-0.0020.0000.0360.0000.0310.0130.0191.000-0.0070.0000.0200.0160.0330.000-0.016-0.0330.0000.0000.0160.007-0.0040.007-0.0170.0000.0030.0000.0000.0000.0000.0050.017
Odometer_reading0.0000.000-0.0020.0370.000-0.0450.0210.0390.0000.0480.0000.0340.018-0.008-0.0210.0290.000-0.0160.0130.0000.0410.0330.0000.0090.0370.000-0.002-0.0170.0330.0000.0000.000-0.0030.000-0.0071.0000.0220.0160.0000.0000.039-0.012-0.0030.0230.0000.041-0.006-0.009-0.006-0.0100.0350.0230.0000.0230.0270.006-0.0130.018
Own_Damage0.0000.0000.0400.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0100.0110.0260.0000.0330.0000.0000.0290.0000.0000.0460.0300.0000.0000.0000.0000.0000.0000.0000.0221.0000.0000.0200.0000.0000.0330.0000.0000.0000.0310.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.000
Parts_replaced0.0000.0000.0000.0000.0150.0000.0000.0240.0000.0000.0190.0000.0000.0180.0260.0180.0090.0000.0000.0130.0000.0360.0090.0000.0000.0000.0000.0110.0000.0190.0000.0000.0000.0000.0200.0160.0001.0000.0120.0000.0000.0130.0000.0000.0000.0150.0000.0000.0000.0000.0180.0000.0000.0280.0000.0000.0220.000
Payment_Frequency0.0260.0000.0000.0000.0150.0060.0350.0370.0380.0000.0000.0000.0000.0000.0000.0000.0000.0170.0190.0000.0380.0000.0000.0130.0200.0000.0000.0220.0000.0000.0000.0260.0140.0150.0160.0000.0200.0121.0000.0070.0000.0170.0000.0200.0000.0320.0180.0000.0180.0000.0000.0000.0000.0000.0000.0000.0240.000
Payment_status0.0180.0210.0000.0000.0170.0000.0190.0100.0000.0000.0200.0290.0250.0390.0320.0000.0000.0450.0290.0000.0000.0000.0090.0000.0000.0120.0000.0190.0230.0000.0000.0000.0000.0000.0330.0000.0000.0000.0071.0000.0000.0000.0000.0000.0340.0000.0140.0000.0140.0000.0110.0040.0000.0520.0180.0000.0440.000
Policy_Status0.0130.0140.0190.0000.0340.0260.0000.0000.0250.0070.0050.0190.0000.0210.0000.0000.0000.0000.0270.0220.0000.0000.0000.0040.0000.0000.0000.0330.0000.0000.0000.0000.0180.0000.0000.0390.0000.0000.0000.0001.0000.0000.0200.0250.0000.0270.0000.0300.0000.0330.0000.0000.0000.0040.0280.0000.0260.000
Premium_amount0.0000.0000.5250.0240.0000.0190.0000.0330.0210.0000.0000.0000.0000.4040.5720.0000.000-0.014-0.0040.0000.0000.0200.0000.0000.0230.0280.5250.7760.0000.0000.0000.034-0.0080.024-0.016-0.0120.0330.0130.0170.0000.0001.0000.0150.0000.0000.0000.510-0.0030.5100.4920.0260.0000.0210.0190.0260.0000.6670.000
Previous_claims0.0310.0200.0190.0000.000-0.0110.0000.0270.0240.0000.0000.0000.0070.0140.0060.0100.0210.0110.0110.0000.0000.0060.0260.0000.0040.0000.0190.0110.0000.0000.0000.000-0.0230.011-0.033-0.0030.0000.0000.0000.0000.0200.0151.0000.0220.0000.0000.0270.0110.0270.0120.0290.0150.0150.0230.0000.0000.0030.000
Previous_traffic_violations0.0000.0070.0070.0070.0000.0360.0000.0000.0410.0170.0000.0140.0000.0000.0000.0170.0000.0240.0000.0140.0410.0150.0200.0000.0000.0290.0140.0000.0000.0170.0000.0000.0000.0170.0000.0230.0000.0000.0200.0000.0250.0000.0221.0000.0000.0030.0200.0170.0200.0000.0000.0000.0060.0000.0000.0000.0200.034
Red_flags0.0110.0280.0000.0210.0000.0170.0000.0210.0160.0150.0070.0000.0130.0000.0000.0000.0000.0360.0000.0310.0000.0000.0000.0000.0230.0190.0000.0000.0240.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0001.0000.0000.0350.0000.0350.0000.0000.0000.0240.0000.0000.0000.0430.000
Regulatory_compliance0.0000.0000.0400.0220.0060.0290.0000.0000.0000.0000.0000.0000.0160.0490.0000.0000.0000.0000.0000.0080.0000.0160.0120.0000.0180.0000.0380.0000.0000.0000.0230.0030.0180.0000.0160.0410.0310.0150.0320.0000.0270.0000.0000.0030.0001.0000.0280.0000.0280.0190.0000.0280.0000.0000.0350.0000.0270.015
Repair_Cost0.0000.0000.9640.0000.0160.0190.0000.0000.0000.0210.0000.0000.0000.7380.4700.0000.000-0.005-0.0020.0060.0000.0450.0290.0080.0230.0170.9640.6300.0200.0380.0000.047-0.0000.0000.007-0.0060.0000.0000.0180.0140.0000.5100.0270.0200.0350.0281.000-0.0031.0000.3920.0200.0000.0000.0000.0000.0000.5520.000
Repair_duration0.0420.008-0.0040.0190.0000.0030.0000.0300.0170.0000.0000.0000.000-0.006-0.0200.0000.019-0.0080.0020.0290.0000.0050.0030.0000.0120.023-0.003-0.0040.0000.0370.0170.030-0.0310.000-0.004-0.0090.0000.0000.0000.0000.030-0.0030.0110.0170.0000.000-0.0031.000-0.0030.0040.0000.0120.0000.0000.0350.000-0.0180.039
Repair_estimates0.0000.0000.9640.0000.0160.0190.0000.0000.0000.0210.0000.0000.0000.7380.4700.0000.000-0.005-0.0020.0060.0000.0450.0290.0080.0230.0170.9640.6300.0200.0380.0000.047-0.0000.0000.007-0.0060.0000.0000.0180.0140.0000.5100.0270.0200.0350.0281.000-0.0031.0000.3920.0200.0000.0000.0000.0000.0000.5520.000
Salvage_value0.0220.0280.4070.0140.000-0.0030.0120.0000.0000.0000.0270.0310.0390.3140.6100.0000.0190.008-0.0000.0300.0300.0000.0000.0100.0340.0240.4070.6100.0080.0000.0000.031-0.0240.000-0.017-0.0100.0260.0000.0000.0000.0330.4920.0120.0000.0000.0190.3920.0040.3921.0000.0370.0370.0090.0000.0000.0000.7030.000
Third_Party0.0070.0000.0000.0000.0120.0270.0110.0100.0000.0120.0230.0000.0000.0220.0000.0100.0180.0160.0000.0310.0000.0040.0000.0360.0160.0450.0000.0000.0060.0000.0000.0000.0000.0000.0000.0350.0000.0180.0000.0110.0000.0260.0290.0000.0000.0000.0200.0000.0200.0371.0000.0000.0000.0000.0000.0000.0000.000
Traffic_condition_at_the_time_of_incident0.0000.0000.0000.0000.0000.0240.0310.0000.0000.0000.0120.0120.0000.0000.0000.0090.0210.0280.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0140.0050.0310.0000.0000.0000.0030.0230.0000.0000.0000.0040.0000.0000.0150.0000.0000.0280.0000.0120.0000.0370.0001.0000.0130.0000.0000.0000.0310.000
Vehicle_condition0.0000.0000.0070.0000.0000.0120.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0120.0000.0000.0000.0190.0150.0000.0000.0120.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0150.0060.0240.0000.0000.0000.0000.0090.0000.0131.0000.0160.0000.0000.0000.000
Vehicle_history_accident_repair_history0.0000.0330.0320.0000.0000.0100.0000.0320.0070.0000.0000.0100.0000.0000.0300.0000.0150.0190.0140.0000.0000.0000.0140.0050.0260.0000.0150.0000.0060.0130.0000.0200.0340.0000.0000.0230.0000.0280.0000.0520.0040.0190.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0161.0000.0000.0300.0000.027
Vehicle_safety_features0.0000.0000.0000.0270.0320.0200.0020.0000.0000.0000.0000.0000.0000.0000.0110.0000.0140.0000.0000.0000.0000.0150.0230.0000.0120.0060.0000.0200.0080.0000.0240.0050.0060.0190.0000.0270.0000.0000.0000.0180.0280.0260.0000.0000.0000.0350.0000.0350.0000.0000.0000.0000.0000.0001.0000.0000.0220.000
Vehicle_type_sedan_SUV_etc0.0000.0000.0000.0000.0310.0130.0000.0320.0000.0200.0140.0340.0000.0310.0000.0000.0260.0250.0000.0250.0000.0000.0000.0000.0000.0000.0110.0150.0190.0000.0200.0030.0240.0250.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0001.0000.0000.000
Vehicle_value_market_value0.0540.0210.5680.0230.0000.0170.0000.0000.0000.0080.0000.0000.0000.4320.8350.0000.000-0.0040.0000.0370.0000.0000.0000.0000.0000.0150.5680.8510.0000.0280.0510.005-0.0090.0000.005-0.0130.0000.0220.0240.0440.0260.6670.0030.0200.0430.0270.552-0.0180.5520.7030.0000.0310.0000.0000.0220.0001.0000.013
Weather_conditions_at_the_time_of_the_accident0.0000.0020.0220.0090.0120.0230.0110.0000.0250.0000.0000.0000.0000.0170.0000.0000.0000.0130.0170.0000.0280.0210.0160.0000.0100.0100.0130.0000.0090.0420.0160.0040.0250.0230.0170.0180.0000.0000.0000.0000.0000.0000.0000.0340.0000.0150.0000.0390.0000.0000.0000.0000.0000.0270.0000.0000.0131.000

Missing values

2025-03-22T22:26:43.656574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-22T22:26:44.528821image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Policy_numberTypeCoverageCoverage_Add_OnsPolicy_StatusInsured_amountPremium_amountIssuance_datePayment_statusPayment_FrequencyPolicy_renewal_dateClaimant_ageClaimant_driver_historyVehicle_conditionVehicle_value_market_valueVehicle_type_sedan_SUV_etcVehicle_history_accident_repair_historyFuel_typeInsurance_coverage_typeOdometer_readingVehicle_safety_featuresLocationNature_of_incidentIncident_severity_levelWeather_conditions_at_the_time_of_the_accidentTraffic_condition_at_the_time_of_incidentIncident_descriptionInvolvement_of_pedestrians_or_animalsCause_of_incidentDamage_to_property_outside_the_vehicleOwn_DamageThird_PartyComprehensiveExtent_of_damageRepair_estimatesRepair_CostDepreciationSalvage_valueParts_replacedInsurance_settlement_amountRepair_durationDriving_LicenseRC_BookClaim_amountDeductiblesPrevious_claimsClaim_statusDisability_statusLoss_of_wagesRed_flagsInvestigationRegulatory_complianceCourt_casesFraud_detectionClaims_approval_statusFinal_settlement_approvalClaim_verificationFraud_detection_approvalDriver_licenseDriver_historyDriving_experiencePrevious_traffic_violationsLicense_expiry_dateAlcohol_drug_test_resultsLicense_class_if_applicableLicense_TypeNumber_of_days
0POL91142091013217882841.9587012025-03-10032025-12-20671 accident13.13691501 accident1088553Airbags, ABSLake Michael321ModerateScratched carYes3NoTrueFalseTrue389422.3111912.3111912.4566862.076175Roof panel1.9097623DL8020609543RC74971528341.9010351.17409610False43001TrueClosedCompliantTrueFailedApprovedTrue11DL1927770604161 parking violation2025-05-2510Permanent253
1POL99025488022012685430.4289412024-07-07102025-12-09312 minor accidents00.7804280No accidents1162446Airbags, ABSAtkinsontown203LightHit by a truckNo1YesTrueFalseTrue405371.1492481.149248-0.3848611.173142Left side mirror2.0351088DL7077290714RC96610803511.9989660.82101251True42868TrueUnder processNon-compliantFalsePassedRejectedFalse10DL8476940969111 speeding2027-01-2811Learning592
2POL236358420031660740-0.6339672024-06-17042025-07-2736Clean0-0.72893621 repair0046778Airbags, ABSPort Vanessa324LightRear-ended carYes0NoTrueFalseTrue20171-1.208156-1.208156-0.564465-0.111614Rear bumper-1.22607810DL0670616572RC4001294311-1.229284-0.89493522False37440TrueUnder processNon-compliantTruePassedApprovedTrue10DL30729397532151 speeding2029-09-1300Learning363
3POL67645878002110731150.5528702024-06-15022026-06-1925One speeding ticket10.26630601 accident0167979Airbags, ABS, Parking SensorsLake Stephanieville423ModeratePedestrian hitNo3NoTrueFalseTrue23617-1.208769-1.2087690.4227861.602588Left fender-1.1179795DL1103018486RC3296984930-1.129294-0.95051231True23778FalsePendingCompliantFalseFailedApprovedTrue01DL80352417353111 parking violation2025-05-2601Learning360
4POL635475380130592923-1.0961642024-08-22122026-03-23212 minor accidents1-0.80418101 repair1183610Airbags, ABSNew James222HeavyPedestrian hitNo3YesFalseFalseTrue17088-1.101964-1.101964-0.575280-0.874440Left fender-1.1288306DL4565815686RC5883571271-1.148253-1.08791550True32174FalseClosedCompliantFalseFailedApprovedFalse01DL17104731240111 parking violation2026-09-2001Learning312
5POL636524280112410763-1.2757462024-10-01142026-11-0561Clean1-0.90960601 accident1065550Airbags, ABS, EBDShieldsberg413ModerateRear-ended carNo3NoFalseTrueTrue46695-0.773545-0.773545-0.337210-1.041696Rear bumper-0.73182410DL0540514387RC4037615510-0.757035-0.90751152False14214TrueUnder processCompliantTruePassedRejectedTrue00DL152498253111No violations2028-02-0811Learning222
6POL7996351201401535163-0.3316982025-03-09122026-10-0232One speeding ticket00.84434911 accident0061735Airbags, ABS, EBDLake Robert104ModerateStolen vehicleYes3NoFalseFalseFalse151321.4455871.4455870.458092-0.828114Left side mirror2.6700502DL2976563578RC77137123012.7089272.46003921False34694FalseUnder processNon-compliantTrueFailedRejectedTrue00DL3617075167162 accidents2026-09-1111Permanent308
8POL959305710110554006-0.4789012024-03-18012026-10-11692 minor accidents1-0.42555721 accident0137879Airbags, ABS, EBDWest Chadstad414HeavyStolen vehicleYes0NoTrueFalseFalse24490-0.895410-0.895410-0.6657760.426528Windshield-0.8038263DL9457306658RC7088645582-0.827599-0.93402120True18041FalseClosedCompliantTrueFailedRejectedTrue01DL7053579378131 speeding2026-03-0400Learning216
9POL8784793302007885040.3770112024-06-08122026-04-1939One speeding ticket0-0.4638722No accidents0189559Airbags, ABS, Reverse SensorsEast Benjamin014LightHit by a truckNo3YesTrueFalseTrue25681-0.310853-0.310853-0.056061-0.944021Front left door-0.2964861DL9018800922RC9196612084-0.327791-0.70566011True26021TrueUnder processCompliantTrueFailedRejectedFalse10DL2015263129020No violations2025-06-2411Permanent493
11POL0659047701029999000.3488222024-08-25032027-03-18452 minor accidents00.4215871No accidents1122593Airbags, ABS, Parking SensorsEast Laurie414HeavyScratched carNo0YesFalseTrueFalse41270-0.598067-0.5980670.462846-0.813729Front left door-0.6143627DL3087333074RC1803236725-0.621433-0.53600641False26496TruePendingNon-compliantTruePassedApprovedFalse11DL47648691710192 accidents2028-05-2610Learning299
Policy_numberTypeCoverageCoverage_Add_OnsPolicy_StatusInsured_amountPremium_amountIssuance_datePayment_statusPayment_FrequencyPolicy_renewal_dateClaimant_ageClaimant_driver_historyVehicle_conditionVehicle_value_market_valueVehicle_type_sedan_SUV_etcVehicle_history_accident_repair_historyFuel_typeInsurance_coverage_typeOdometer_readingVehicle_safety_featuresLocationNature_of_incidentIncident_severity_levelWeather_conditions_at_the_time_of_the_accidentTraffic_condition_at_the_time_of_incidentIncident_descriptionInvolvement_of_pedestrians_or_animalsCause_of_incidentDamage_to_property_outside_the_vehicleOwn_DamageThird_PartyComprehensiveExtent_of_damageRepair_estimatesRepair_CostDepreciationSalvage_valueParts_replacedInsurance_settlement_amountRepair_durationDriving_LicenseRC_BookClaim_amountDeductiblesPrevious_claimsClaim_statusDisability_statusLoss_of_wagesRed_flagsInvestigationRegulatory_complianceCourt_casesFraud_detectionClaims_approval_statusFinal_settlement_approvalClaim_verificationFraud_detection_approvalDriver_licenseDriver_historyDriving_experiencePrevious_traffic_violationsLicense_expiry_dateAlcohol_drug_test_resultsLicense_class_if_applicableLicense_TypeNumber_of_days
4990POL305806000210321411-0.8415322024-08-13142026-12-2069Clean0-1.05981001 accident0173876Airbags, ABS, Parking SensorsChavezstad302HeavyPedestrian hitYes1NoTrueTrueFalse43757-0.914285-0.914285-0.783520-0.671982Rear bumper-0.8041426DL7426975191RC6145910449-0.813453-0.70247722False35500FalseUnder processCompliantFalsePassedRejectedFalse11DL06265576683151 speeding2029-09-0111Learning168
4991POL920935260040832022-0.7796712024-10-18122025-12-22202 minor accidents1-0.1190412No accidents1045242Airbags, ABS, EBDAlexandramouth303LightScratched carNo2NoTrueTrueFalse150280.7547260.754726-0.612807-1.003368Left side mirror0.7367991DL2352832899RC76092416180.8439972.22478911False47046TruePendingNon-compliantTrueFailedRejectedFalse00DL11211489163181 speeding2026-09-0700Permanent281
4992POL39532720012218150483.0704362025-01-13042026-04-24232 minor accidents11.74975311 accident0168180Airbags, ABS, Reverse SensorsEast Francesport413LightStolen vehicleNo2YesFalseFalseTrue277030.4926770.4926772.3628970.220863Left side mirror0.6765039DL9994345915RC71145529790.7669461.91481021True34179TrueClosedNon-compliantTruePassedRejectedFalse01DL3632754162220No violations2029-05-0811Learning432
4993POL67999350020017770802.1334742024-05-05012027-03-04221 accident10.47736921 repair1056236Airbags, ABS, EBDReynoldsburgh104HeavyHit by a truckYes1NoTrueFalseFalse393670.2001550.200155-0.0249491.122310Roof panel-0.0130611DL5557569536RC27894132120.0269120.63158120True30476FalseUnder processCompliantTruePassedApprovedFalse01DL9215521935261 speeding2030-01-2311Permanent366
4994POL89996613002110271620.1005522024-10-29122026-04-1467One speeding ticket00.45913821 repair0049033Airbags, ABS, Reverse SensorsNew Theresaport013ModerateHit by a truckNo3NoTrueFalseFalse354451.7404761.7404760.291816-0.390660Left side mirror1.0417458DL6797043882RC55613850631.1463762.39345150False29872FalseUnder processNon-compliantTruePassedApprovedFalse10DL61126812743131 parking violation2026-08-1410Learning123
4995POL114092210232337237-0.7816362024-10-23102026-11-19502 minor accidents0-1.4423760No accidents0152415Airbags, ABSNew Rhondaberg024ModeratePedestrian hitNo0YesTrueTrueTrue45441-1.099414-1.099414-1.317728-0.988039Left side mirror-0.93950210DL3951843345RC4320658495-0.966227-1.07466040False43392TrueClosedCompliantTruePassedApprovedFalse11DL3169899859361 speeding2028-02-2500Permanent591
4996POL82981679022118795332.1922312024-05-25112026-06-01542 minor accidents01.55235521 repair1068690Airbags, ABS, Reverse SensorsBrianstad401LightPedestrian hitNo3YesFalseFalseTrue207601.8194071.8194072.7305232.813459Left side mirror1.8267269DL0753073487RC46790374541.7599550.18680801True44018FalseClosedCompliantFalseFailedRejectedTrue11DL945917444422No violations2027-11-1210Permanent457
4997POL396871510241393402-1.0309412024-05-31012026-02-18441 accident1-1.04091921 accident0134504Airbags, ABSHannahstad204HeavyStolen vehicleNo0YesTrueTrueTrue38300-1.202576-1.202576-0.502655-0.931232Rear bumper-1.2023968DL6471626174RC4896238403-1.223656-1.16796740False13581FalseClosedCompliantTrueFailedRejectedFalse00DL8124353380221 speeding2030-02-1510Permanent273
4998POL477657870142438323-0.8720482024-10-07002026-07-14521 accident0-0.97011621 accident0082407Airbags, ABS, Parking SensorsFrederickchester021LightPedestrian hitNo3YesFalseFalseTrue33527-0.705796-0.705796-0.666540-1.029920Left side mirror-0.9230542DL4088810832RC3330563246-0.910870-0.43982930True46392FalseClosedCompliantFalsePassedApprovedTrue10DL036416370007No violations2026-11-2700Permanent554
4999POL69507199014217036200.6304542024-05-10012026-04-02342 minor accidents11.63096001 accident0023831Airbags, ABS, Reverse SensorsMoonville402HeavyHit by a truckNo0NoFalseFalseTrue160491.0970561.0970562.7669171.376949Rear bumper2.06479910DL6324791879RC48920906502.0086440.52074002False24576TrueClosedCompliantFalsePassedRejectedTrue10DL70191592253121 speeding2027-04-0800Learning638